{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xlrd\n",
      "  Downloading xlrd-1.2.0-py2.py3-none-any.whl (103 kB)\n",
      "Installing collected packages: xlrd\n",
      "Successfully installed xlrd-1.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip install xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting parse\n",
      "  Downloading parse-1.18.0.tar.gz (30 kB)\n",
      "Building wheels for collected packages: parse\n",
      "  Building wheel for parse (setup.py): started\n",
      "  Building wheel for parse (setup.py): finished with status 'done'\n",
      "  Created wheel for parse: filename=parse-1.18.0-py3-none-any.whl size=24138 sha256=ba8413a5cff9a7228d4fef20a692d99c25d601568fb4d85ebaf3c01cb7e7b449\n",
      "  Stored in directory: c:\\users\\14686\\appdata\\local\\pip\\cache\\wheels\\05\\d6\\62\\d7e3a31cb5ac82c57423e083738882b792a9108b30cc05f579\n",
      "Successfully built parse\n",
      "Installing collected packages: parse\n",
      "Successfully installed parse-1.18.0\n"
     ]
    }
   ],
   "source": [
    "!pip install parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import parse\n",
    "\n",
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"附件一：325个样本数据.xlsx\", skiprows=[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = pd.Series(df.columns)\n",
    "columns[:2] = [\"sample_index\", \"time\"]\n",
    "df.columns = columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>sample_index</th>\n",
       "      <th>time</th>\n",
       "      <th>硫含量,μg/g</th>\n",
       "      <th>辛烷值RON</th>\n",
       "      <th>饱和烃,v%（烷烃+环烷烃）</th>\n",
       "      <th>烯烃,v%</th>\n",
       "      <th>芳烃,v%</th>\n",
       "      <th>溴值\\n,gBr/100g</th>\n",
       "      <th>密度(20℃),\\nkg/m³</th>\n",
       "      <th>硫含量,μg/g.1</th>\n",
       "      <th>...</th>\n",
       "      <th>再生器藏量</th>\n",
       "      <th>加氢裂化轻石脑油进装置流量</th>\n",
       "      <th>汽油产品去气分流量</th>\n",
       "      <th>加氢裂化轻石脑油进装置累积流量</th>\n",
       "      <th>汽油产品去气分累积流量</th>\n",
       "      <th>8.0MPa氢气至循环氢压缩机入口</th>\n",
       "      <th>8.0MPa氢气至循环氢压缩机入口.1</th>\n",
       "      <th>8.0MPa氢气至反吹氢压缩机出口</th>\n",
       "      <th>8.0MPa氢气至反吹氢压缩机出口.1</th>\n",
       "      <th>D101原料缓冲罐压力</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2020/5/26 8:00:00</td>\n",
       "      <td>188.0</td>\n",
       "      <td>90.6</td>\n",
       "      <td>53.230000</td>\n",
       "      <td>24.400000</td>\n",
       "      <td>22.370000</td>\n",
       "      <td>61.487143</td>\n",
       "      <td>726.085714</td>\n",
       "      <td>3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.045436</td>\n",
       "      <td>6368.74700</td>\n",
       "      <td>233.310805</td>\n",
       "      <td>83086802.0</td>\n",
       "      <td>832503.795</td>\n",
       "      <td>2216.40935</td>\n",
       "      <td>39063124.5</td>\n",
       "      <td>1840.14470</td>\n",
       "      <td>39608757.0</td>\n",
       "      <td>0.353271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2020/5/21 8:00:00</td>\n",
       "      <td>169.0</td>\n",
       "      <td>90.5</td>\n",
       "      <td>52.300000</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>61.880000</td>\n",
       "      <td>731.300000</td>\n",
       "      <td>3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.025974</td>\n",
       "      <td>6360.64530</td>\n",
       "      <td>242.369205</td>\n",
       "      <td>82318954.0</td>\n",
       "      <td>803462.665</td>\n",
       "      <td>2370.58740</td>\n",
       "      <td>38810581.5</td>\n",
       "      <td>1641.73260</td>\n",
       "      <td>39389299.0</td>\n",
       "      <td>0.354504</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2020/5/19 8:00:00</td>\n",
       "      <td>177.0</td>\n",
       "      <td>90.7</td>\n",
       "      <td>52.300000</td>\n",
       "      <td>26.314286</td>\n",
       "      <td>21.385714</td>\n",
       "      <td>61.722857</td>\n",
       "      <td>729.614286</td>\n",
       "      <td>3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.964696</td>\n",
       "      <td>6504.96490</td>\n",
       "      <td>233.076925</td>\n",
       "      <td>82012004.0</td>\n",
       "      <td>791925.055</td>\n",
       "      <td>2326.46535</td>\n",
       "      <td>38693812.0</td>\n",
       "      <td>1600.67575</td>\n",
       "      <td>39312616.5</td>\n",
       "      <td>0.350181</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2020/5/14 8:00:00</td>\n",
       "      <td>159.0</td>\n",
       "      <td>90.4</td>\n",
       "      <td>52.300000</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>61.330000</td>\n",
       "      <td>725.400000</td>\n",
       "      <td>3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.039041</td>\n",
       "      <td>6506.82495</td>\n",
       "      <td>238.349935</td>\n",
       "      <td>81231373.5</td>\n",
       "      <td>762863.810</td>\n",
       "      <td>2495.22360</td>\n",
       "      <td>38410862.5</td>\n",
       "      <td>1563.71215</td>\n",
       "      <td>39120204.5</td>\n",
       "      <td>0.353930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2020/5/12 8:00:00</td>\n",
       "      <td>173.0</td>\n",
       "      <td>89.6</td>\n",
       "      <td>52.242857</td>\n",
       "      <td>26.671429</td>\n",
       "      <td>21.085714</td>\n",
       "      <td>61.332857</td>\n",
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       "      <td>3.2</td>\n",
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       "      <td>1.986900</td>\n",
       "      <td>6560.24225</td>\n",
       "      <td>236.576220</td>\n",
       "      <td>80915707.5</td>\n",
       "      <td>751362.300</td>\n",
       "      <td>2807.78910</td>\n",
       "      <td>38283000.0</td>\n",
       "      <td>1554.35740</td>\n",
       "      <td>39045953.5</td>\n",
       "      <td>0.358053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 370 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample_index               time  硫含量,μg/g  辛烷值RON  饱和烃,v%（烷烃+环烷烃）  \\\n",
       "0             1  2020/5/26 8:00:00     188.0    90.6       53.230000   \n",
       "1             2  2020/5/21 8:00:00     169.0    90.5       52.300000   \n",
       "2             3  2020/5/19 8:00:00     177.0    90.7       52.300000   \n",
       "3             4  2020/5/14 8:00:00     159.0    90.4       52.300000   \n",
       "4             5  2020/5/12 8:00:00     173.0    89.6       52.242857   \n",
       "\n",
       "       烯烃,v%      芳烃,v%  溴值\\n,gBr/100g  密度(20℃),\\nkg/m³  硫含量,μg/g.1  ...  \\\n",
       "0  24.400000  22.370000      61.487143       726.085714         3.2  ...   \n",
       "1  26.400000  21.300000      61.880000       731.300000         3.2  ...   \n",
       "2  26.314286  21.385714      61.722857       729.614286         3.2  ...   \n",
       "3  26.100000  21.600000      61.330000       725.400000         3.2  ...   \n",
       "4  26.671429  21.085714      61.332857       725.428571         3.2  ...   \n",
       "\n",
       "      再生器藏量  加氢裂化轻石脑油进装置流量   汽油产品去气分流量  加氢裂化轻石脑油进装置累积流量  汽油产品去气分累积流量  \\\n",
       "0  2.045436     6368.74700  233.310805       83086802.0   832503.795   \n",
       "1  2.025974     6360.64530  242.369205       82318954.0   803462.665   \n",
       "2  1.964696     6504.96490  233.076925       82012004.0   791925.055   \n",
       "3  2.039041     6506.82495  238.349935       81231373.5   762863.810   \n",
       "4  1.986900     6560.24225  236.576220       80915707.5   751362.300   \n",
       "\n",
       "   8.0MPa氢气至循环氢压缩机入口  8.0MPa氢气至循环氢压缩机入口.1  8.0MPa氢气至反吹氢压缩机出口  \\\n",
       "0         2216.40935           39063124.5         1840.14470   \n",
       "1         2370.58740           38810581.5         1641.73260   \n",
       "2         2326.46535           38693812.0         1600.67575   \n",
       "3         2495.22360           38410862.5         1563.71215   \n",
       "4         2807.78910           38283000.0         1554.35740   \n",
       "\n",
       "   8.0MPa氢气至反吹氢压缩机出口.1  D101原料缓冲罐压力  \n",
       "0           39608757.0     0.353271  \n",
       "1           39389299.0     0.354504  \n",
       "2           39312616.5     0.350181  \n",
       "3           39120204.5     0.353930  \n",
       "4           39045953.5     0.358053  \n",
       "\n",
       "[5 rows x 370 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对325个样本的分析与处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 原料性质"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>硫含量,μg/g</th>\n",
       "      <th>辛烷值RON</th>\n",
       "      <th>饱和烃,v%（烷烃+环烷烃）</th>\n",
       "      <th>烯烃,v%</th>\n",
       "      <th>芳烃,v%</th>\n",
       "      <th>溴值\\n,gBr/100g</th>\n",
       "      <th>密度(20℃),\\nkg/m³</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>188.0</td>\n",
       "      <td>90.6</td>\n",
       "      <td>53.230000</td>\n",
       "      <td>24.400000</td>\n",
       "      <td>22.370000</td>\n",
       "      <td>61.487143</td>\n",
       "      <td>726.085714</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169.0</td>\n",
       "      <td>90.5</td>\n",
       "      <td>52.300000</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>61.880000</td>\n",
       "      <td>731.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>177.0</td>\n",
       "      <td>90.7</td>\n",
       "      <td>52.300000</td>\n",
       "      <td>26.314286</td>\n",
       "      <td>21.385714</td>\n",
       "      <td>61.722857</td>\n",
       "      <td>729.614286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>159.0</td>\n",
       "      <td>90.4</td>\n",
       "      <td>52.300000</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>61.330000</td>\n",
       "      <td>725.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>173.0</td>\n",
       "      <td>89.6</td>\n",
       "      <td>52.242857</td>\n",
       "      <td>26.671429</td>\n",
       "      <td>21.085714</td>\n",
       "      <td>61.332857</td>\n",
       "      <td>725.428571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   硫含量,μg/g  辛烷值RON  饱和烃,v%（烷烃+环烷烃）      烯烃,v%      芳烃,v%  溴值\\n,gBr/100g  \\\n",
       "0     188.0    90.6       53.230000  24.400000  22.370000      61.487143   \n",
       "1     169.0    90.5       52.300000  26.400000  21.300000      61.880000   \n",
       "2     177.0    90.7       52.300000  26.314286  21.385714      61.722857   \n",
       "3     159.0    90.4       52.300000  26.100000  21.600000      61.330000   \n",
       "4     173.0    89.6       52.242857  26.671429  21.085714      61.332857   \n",
       "\n",
       "   密度(20℃),\\nkg/m³  \n",
       "0       726.085714  \n",
       "1       731.300000  \n",
       "2       729.614286  \n",
       "3       725.400000  \n",
       "4       725.428571  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,2:9].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(325, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,2:9].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 产品性质（2标签）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>硫含量,μg/g.1</th>\n",
       "      <th>辛烷值RON.1</th>\n",
       "      <th>RON损失\\n（不是变量）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.22</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.32</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.32</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.02</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.2</td>\n",
       "      <td>88.32</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   硫含量,μg/g.1  辛烷值RON.1  RON损失\\n（不是变量）\n",
       "0         3.2     89.22           1.38\n",
       "1         3.2     89.32           1.18\n",
       "2         3.2     89.32           1.38\n",
       "3         3.2     89.02           1.38\n",
       "4         3.2     88.32           1.28"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,9:12].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 待生吸附剂性质"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>焦炭,wt%</th>\n",
       "      <th>S, wt%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.32</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.37</td>\n",
       "      <td>7.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.43</td>\n",
       "      <td>7.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.08</td>\n",
       "      <td>7.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.45</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   焦炭,wt%  S, wt%\n",
       "0    2.32    7.30\n",
       "1    2.37    7.34\n",
       "2    2.43    7.27\n",
       "3    3.08    7.35\n",
       "4    2.45    6.58"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,12:14].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 再生吸附剂性质"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>焦炭,wt%.1</th>\n",
       "      <th>S, wt%.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.84</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.55</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.89</td>\n",
       "      <td>5.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.98</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.83</td>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   焦炭,wt%.1  S, wt%.1\n",
       "0      1.84      5.98\n",
       "1      0.55      4.38\n",
       "2      1.89      5.82\n",
       "3      0.98      4.67\n",
       "4      0.83      4.52"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,14:16].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 354个操作变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>氢油比</th>\n",
       "      <th>反应过滤器压差</th>\n",
       "      <th>还原器压力</th>\n",
       "      <th>还原器流化氢气流量</th>\n",
       "      <th>反应器上部温度</th>\n",
       "      <th>反应器底部温度</th>\n",
       "      <th>反应器顶部压力</th>\n",
       "      <th>反应器顶底压差</th>\n",
       "      <th>反应过滤器压差.1</th>\n",
       "      <th>反吹氢气温度</th>\n",
       "      <th>...</th>\n",
       "      <th>再生器藏量</th>\n",
       "      <th>加氢裂化轻石脑油进装置流量</th>\n",
       "      <th>汽油产品去气分流量</th>\n",
       "      <th>加氢裂化轻石脑油进装置累积流量</th>\n",
       "      <th>汽油产品去气分累积流量</th>\n",
       "      <th>8.0MPa氢气至循环氢压缩机入口</th>\n",
       "      <th>8.0MPa氢气至循环氢压缩机入口.1</th>\n",
       "      <th>8.0MPa氢气至反吹氢压缩机出口</th>\n",
       "      <th>8.0MPa氢气至反吹氢压缩机出口.1</th>\n",
       "      <th>D101原料缓冲罐压力</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.276318</td>\n",
       "      <td>31.211968</td>\n",
       "      <td>2.526857</td>\n",
       "      <td>647.752380</td>\n",
       "      <td>424.615900</td>\n",
       "      <td>421.608145</td>\n",
       "      <td>2.393991</td>\n",
       "      <td>101.290580</td>\n",
       "      <td>33.480159</td>\n",
       "      <td>240.088445</td>\n",
       "      <td>...</td>\n",
       "      <td>2.045436</td>\n",
       "      <td>6368.74700</td>\n",
       "      <td>233.310805</td>\n",
       "      <td>83086802.0</td>\n",
       "      <td>832503.795</td>\n",
       "      <td>2216.40935</td>\n",
       "      <td>39063124.5</td>\n",
       "      <td>1840.14470</td>\n",
       "      <td>39608757.0</td>\n",
       "      <td>0.353271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.277127</td>\n",
       "      <td>31.461969</td>\n",
       "      <td>2.527411</td>\n",
       "      <td>651.821920</td>\n",
       "      <td>420.953385</td>\n",
       "      <td>417.927830</td>\n",
       "      <td>2.394113</td>\n",
       "      <td>101.544925</td>\n",
       "      <td>33.324023</td>\n",
       "      <td>239.809820</td>\n",
       "      <td>...</td>\n",
       "      <td>2.025974</td>\n",
       "      <td>6360.64530</td>\n",
       "      <td>242.369205</td>\n",
       "      <td>82318954.0</td>\n",
       "      <td>803462.665</td>\n",
       "      <td>2370.58740</td>\n",
       "      <td>38810581.5</td>\n",
       "      <td>1641.73260</td>\n",
       "      <td>39389299.0</td>\n",
       "      <td>0.354504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.277013</td>\n",
       "      <td>31.339998</td>\n",
       "      <td>2.528865</td>\n",
       "      <td>650.747150</td>\n",
       "      <td>423.941065</td>\n",
       "      <td>420.003515</td>\n",
       "      <td>2.394545</td>\n",
       "      <td>101.618320</td>\n",
       "      <td>33.251831</td>\n",
       "      <td>239.742695</td>\n",
       "      <td>...</td>\n",
       "      <td>1.964696</td>\n",
       "      <td>6504.96490</td>\n",
       "      <td>233.076925</td>\n",
       "      <td>82012004.0</td>\n",
       "      <td>791925.055</td>\n",
       "      <td>2326.46535</td>\n",
       "      <td>38693812.0</td>\n",
       "      <td>1600.67575</td>\n",
       "      <td>39312616.5</td>\n",
       "      <td>0.350181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.277222</td>\n",
       "      <td>31.033443</td>\n",
       "      <td>2.530589</td>\n",
       "      <td>652.669055</td>\n",
       "      <td>423.722805</td>\n",
       "      <td>419.964775</td>\n",
       "      <td>2.392854</td>\n",
       "      <td>105.029760</td>\n",
       "      <td>33.197268</td>\n",
       "      <td>239.732465</td>\n",
       "      <td>...</td>\n",
       "      <td>2.039041</td>\n",
       "      <td>6506.82495</td>\n",
       "      <td>238.349935</td>\n",
       "      <td>81231373.5</td>\n",
       "      <td>762863.810</td>\n",
       "      <td>2495.22360</td>\n",
       "      <td>38410862.5</td>\n",
       "      <td>1563.71215</td>\n",
       "      <td>39120204.5</td>\n",
       "      <td>0.353930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.277310</td>\n",
       "      <td>30.826894</td>\n",
       "      <td>2.530938</td>\n",
       "      <td>649.340920</td>\n",
       "      <td>427.132000</td>\n",
       "      <td>422.128935</td>\n",
       "      <td>2.392715</td>\n",
       "      <td>105.937455</td>\n",
       "      <td>32.785944</td>\n",
       "      <td>239.835635</td>\n",
       "      <td>...</td>\n",
       "      <td>1.986900</td>\n",
       "      <td>6560.24225</td>\n",
       "      <td>236.576220</td>\n",
       "      <td>80915707.5</td>\n",
       "      <td>751362.300</td>\n",
       "      <td>2807.78910</td>\n",
       "      <td>38283000.0</td>\n",
       "      <td>1554.35740</td>\n",
       "      <td>39045953.5</td>\n",
       "      <td>0.358053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 354 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        氢油比    反应过滤器压差     还原器压力   还原器流化氢气流量     反应器上部温度     反应器底部温度  \\\n",
       "0  0.276318  31.211968  2.526857  647.752380  424.615900  421.608145   \n",
       "1  0.277127  31.461969  2.527411  651.821920  420.953385  417.927830   \n",
       "2  0.277013  31.339998  2.528865  650.747150  423.941065  420.003515   \n",
       "3  0.277222  31.033443  2.530589  652.669055  423.722805  419.964775   \n",
       "4  0.277310  30.826894  2.530938  649.340920  427.132000  422.128935   \n",
       "\n",
       "    反应器顶部压力     反应器顶底压差  反应过滤器压差.1      反吹氢气温度  ...     再生器藏量  加氢裂化轻石脑油进装置流量  \\\n",
       "0  2.393991  101.290580  33.480159  240.088445  ...  2.045436     6368.74700   \n",
       "1  2.394113  101.544925  33.324023  239.809820  ...  2.025974     6360.64530   \n",
       "2  2.394545  101.618320  33.251831  239.742695  ...  1.964696     6504.96490   \n",
       "3  2.392854  105.029760  33.197268  239.732465  ...  2.039041     6506.82495   \n",
       "4  2.392715  105.937455  32.785944  239.835635  ...  1.986900     6560.24225   \n",
       "\n",
       "    汽油产品去气分流量  加氢裂化轻石脑油进装置累积流量  汽油产品去气分累积流量  8.0MPa氢气至循环氢压缩机入口  \\\n",
       "0  233.310805       83086802.0   832503.795         2216.40935   \n",
       "1  242.369205       82318954.0   803462.665         2370.58740   \n",
       "2  233.076925       82012004.0   791925.055         2326.46535   \n",
       "3  238.349935       81231373.5   762863.810         2495.22360   \n",
       "4  236.576220       80915707.5   751362.300         2807.78910   \n",
       "\n",
       "   8.0MPa氢气至循环氢压缩机入口.1  8.0MPa氢气至反吹氢压缩机出口  8.0MPa氢气至反吹氢压缩机出口.1  D101原料缓冲罐压力  \n",
       "0           39063124.5         1840.14470           39608757.0     0.353271  \n",
       "1           38810581.5         1641.73260           39389299.0     0.354504  \n",
       "2           38693812.0         1600.67575           39312616.5     0.350181  \n",
       "3           38410862.5         1563.71215           39120204.5     0.353930  \n",
       "4           38283000.0         1554.35740           39045953.5     0.358053  \n",
       "\n",
       "[5 rows x 354 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,16:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 空值的分布情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每列缺失值分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq_0.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺失率超过 1/6 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[eq_0>50].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "ops = df.iloc[:, 15:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "eq_0 = (ops==0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "ops.columns = range(354)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 12,  23,  24,  25,  27,  45,  46,  47,  73,  77,  88,  95, 109,\n",
       "            134, 150, 161, 162, 163, 196, 204, 207, 215, 222, 283, 298, 331,\n",
       "            345, 346, 347, 350, 352],\n",
       "           dtype='int64')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H = 1; display(ops.columns[eq_0>H]); ops.columns[eq_0>H].size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化空值列的情况（从分布和时序变化情况入手）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "many 0 n_columns = 32\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 12\n",
      "n_zeros =  145\n",
      "mean =  96.29557126602798\n",
      "var =  8800.840702509338\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 23\n",
      "n_zeros =  4\n",
      "mean =  1095.7071060990706\n",
      "var =  382595.7353741357\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 24\n",
      "n_zeros =  288\n",
      "mean =  129.7268864630769\n",
      "var =  271357.1711993013\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 25\n",
      "n_zeros =  126\n",
      "mean =  883.2041032387297\n",
      "var =  726706.7969269729\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 27\n",
      "n_zeros =  134\n",
      "mean =  3.2396497526297208\n",
      "var =  36.16237101296654\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 38\n",
      "n_zeros =  1\n",
      "mean =  470.7497458040931\n",
      "var =  19475.821061347993\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 45\n",
      "n_zeros =  137\n",
      "mean =  71.79670307692307\n",
      "var =  4407.875120162856\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 46\n",
      "n_zeros =  4\n",
      "mean =  51.84964109210481\n",
      "var =  62.8411887965605\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 47\n",
      "n_zeros =  19\n",
      "mean =  66.01853417695237\n",
      "var =  508.3994953655987\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 73\n",
      "n_zeros =  219\n",
      "mean =  15.475980565199968\n",
      "var =  1698.1541251379344\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 77\n",
      "n_zeros =  214\n",
      "mean =  5.031604830576924\n",
      "var =  83.06388348187838\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 88\n",
      "n_zeros =  187\n",
      "mean =  256.56256026153847\n",
      "var =  89679.27871350407\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 95\n",
      "n_zeros =  123\n",
      "mean =  2417178.651207692\n",
      "var =  87635559112242.06\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 109\n",
      "n_zeros =  109\n",
      "mean =  81.49407479282796\n",
      "var =  4808.137432647388\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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UYX7DxJPvbN7cewgYeZClfFEolZzEHE0YoYfnQvFb3VHlZctJpZ+Q9cCNiYmq9gA97vIGnMn44D4DwGU1HViJjFcL/IqbnmDhjEn8w385IXKfdFrZvt+xxp/auIePvGNhUYk8/+0nz7Lv0DA/+OQ7ih5PKqtMbdGHZdEUdy3wYlwoZZzfLHDDaDDGqX7zn+t3cduTm/Puk0wrfW639zuf3sKvXtqelcgTdJl49+quZ7by8Cs7Snr4ZQl40UdlE3f9Jf6KhFEuFGtqbBgTgPEq4MUQtLA37zmYU0LWT1AUdxwovoO937IfHE6XVYOmKVa8BW5NjQ1jAjAav26j46Woe8RE8jYxDt6qtVv2FXWdZCrN7zaN1Ob7w+8+mknOKYVESBRKlH6bBW4Y45Dgx3riyjccCgi4SG4TBz+eKC6dMxmAtVuLE/B/+MUrrPjpc1nr/v3ZN0sZKuAPI/S7UMwHbhgTlglsgDMwnG1tCzCcV8Cd321uJuT63r6irvPYa5UJ/Q8PI4yIQjEL3DDGH8GP+3iNQimGoAtFREim0pnJwiDevfLEccvug0Vdp29wOHT9C1v3sePAQORxQT95WBhhZBy4+cANY/wzceU73IUynErTGtH5xtNTz03+RrECPhBeafTi7zzKtT9bG3nccCCYOxEyiRkVB25d6Q1jAjCBDfCclmnOJKZG1t32LG9PHPccHGZ3/1Dea+w9OMSeg+EWOOR3rwT98ZlJzKwxhx9bzp/VEnnqmEVllr3deN1FFR6JMZYEP9jjPQpl9YZd7Dk4xPnHz83ZlmOB41jgbQUEPK3K1LYm9h0a5oWt+3jP26MLkL3zuofzjs8T5TCCtVi8MEKKCCMsJ0zRLHDDaDDGt3zDR1Y9wWd+9LvQbVFhhC1N4VLmPevSCifMnwrAC2/mj0QJ9tUMcmAgyTNv7AndlgyENIb7wC0KxTAmLBNlEtPLuPQTFHDEsXqLtcAXzpjEC0WGEubjj/7lMZ7fsjdnfTEuFIlQ3arUAzcMY2zJjUIZk2HUnA0hIX9hYYRDyTStEQLu3StVx/I9fl4HL765vyLju+S7/5mzLhjSODKJWYQFbpOYhjE+SKeVK29ezSPrcrv9TJSGDmGTjUEfODg9MVsjXCh+CzwmsGTWZLbsOZTj6qgUwfN64Y2xLB94+LHVbGpsGEYN2T8wzCPrdvL8ln05qdcTxQIPRpyErUurMpxSWhNRLpSR/WIiLJwxiVRaeWvfAAtmTKr4mIOTmB7ZxayifOBmgRvGuOCAG4c8uSXXxpooHXn6QwQ86ANPpZ0mDq3NUS4U1wJPO24MT7Q37QqPB//GL18dzZCj0/oL1EI595g5dC2aUfL1zAI3jDpk3yEnDnlyS4L+oezJvPE4iRn2ng4NFZ7ETKkylEoXtMDVdaEs7XRqoryybT/vWjorZ/8bel4rdehZRBXW8rtN/Nb4QyveSyqtvL1zSlnXK9oCF5G4iDwjIve6rxeLyGoRWS8iPxGR8FbLhmGUzP4BV8BbE7mTmLUfzqh57LWdeeOcwzaFWeBBH3gqlXbiwJsL+cAd18WsyS0c1tEaGYnSFmHJF4u/BO2Xzl2aWZaIVPq3zZ5ctnhDaS6ULwIv+15/Dfimqh4J7AGuLnsUhmFksd9ngedUI2wwC/y3v+/lY99fzapHNkTuE+Z6CIvHDkahJNNKWsljgfsmMV21O37e1MiqhJNGKeD+9m5nHz0ns1xMMatyKErARWQ+cBFwk/tacLp73+nucgvwgYqNyjAmOBkXSmuul7PB9DvzXsLipj3CsksPhsSBBy1wz6VSKIwwrSNW8PHzOtiws5/+kPNPas7vVT7u8A7e7XO9HAy4efyTmNl9MAldHi3F+sC/BXwZ8Gz9mcBeVfVGvwWYF3agiCwHlgN0dnbS09MTeoG+vr7IbZVmxbLwQjWl0NlWmfNUg7D7WMv7O1oaaazVYv8h539rSsgkZoPpN1PbmoARIffYvn+AWZNbiMck3AIPCRkM+sA9i7xQGKHnAwc4Zm4HqrB+Rx8nLpiWtX/Ug8Djlk+dxobefh5ZtxOA9/2fHlb/xbmZ7f72bv4KicU0NS6HggIuIhcDO1T1aRHpLvUCqroKWAXQ1dWl3d3hp+jp6SFqW6X5ZJk1RvysWJbkG2vrcw544xXdOetqeX9HSyONtRrs6hvk7+53vJVhgtJotVC89+AX8N4Dg5z+9w/xp+9dwrUXHEMq8J6mT2oKtcD3BopMHSpggWeqEbphhABHzHQiUTbvOZgRcFXliQ27SUQFabs0J2JZtVC8BsvB8QAFz1UJinGhnAVcIiIbgdtxXCfXA9NExFOw+cDWqozQMCYY//Tg7zPLadWGz8T0HjhBAQf4zatOolJwgnNGe3No0k7Qd33zo68D0ZOPI2GEIwK+YLoj4P7Ssr94YRsf/f4TBTv2NMfzS+b2/SO1wmOxcBdKJSko4Kp6rarOV9VFwOXAw6p6BfBr4EPublcBd1dniIYxsRhMjkyEpdK56R0Npt8ZAfdbz57LxLNmgy6U1qZ4Zl0xVfqihPXBl7YDI6n0AO0tCWZNbmZDbz+Prd/JUxt382KBAlceTfFY3gfoPc+NtF2LF5E+P1pGk8jz58A1IrIexyd+c2WGZBiVQ0RaReRJEXlORF4Uka+460PDYEWkxX293t2+qNZj9gvEravfyHEbNFoUiheY4X8weY0P4rEY+weGOfWrv8psi8eEREwyE4K3PvlG1vk+etqCnGtEWeC3rnaOTft84OD4wV98cz8fu2k1l934OD98fFNR7yUeE46cPTl029a9h3jy9d2h26rlTClJwFW1R1Uvdpc3qOppqnqkql6mqoOFjjeMMWAQOFtVTwROAs4XkTOIDoO9Gtjjrv+mu19NKeTjbjD9Hnk/vnFnLPCY8ObeQ1n7x0WyJjY37ezP2t6SiHPXZ9+Zte59R80hH2nNdmksmzeVddsPZF4fiOjA4+cvLzwGgKmTmvifFx2T817Colo8Kjlx6cdS6Y1xjTp4Ze2a3B8lOgz2Uvc17vZzpFqfvgj2HMzfMabhLPBAX0qAf/71esAR8KDwxWKQiMdIulb6vOltWdtbEjFOXjg98/qvLj6W9pBoneAY/H/FZfOmZiXdFMOfvGdJZtn/bcKL2R8cji6QVRcWuGE0Im4W8bPADuBB4DWiw2DnAZsB3O37cFyEwXMuF5E1IrKmtze3YuBo2NWXX8AbrRZKmID3uJOXibjkuIgSsRgJnwUefL/Ngf6XTXk65Hj4feDgJPOMBr+Ae3XLh1LZk67+YY/ZJKZhNDqqmlLVk3CipU4Djq7AOVepapeqds2eHd2eqxx29w/xwVPmR1+7olerPp4PPGzc8Vgsp/9kTBxfczIwiekJdUtAwBMFIkMg1wc+f3pbVtbl3/zhsQXP4edDvr+PJ+CeBe6FD/ofNOZCMYxRoqp7caKnziQ6DHYrsADA3T4ViO5iW/kxsrNvkJmTo0sLNZoLJZVJpsndlogJewMuI28Sc8QCd357FnTQAo/nibf2NqV9ceDgCOr5xx+WeT1jckvOsR86dT7//rmzQs+7cOYkfvip0wCfgLuFrL7/iS6+89GTmTetLfTYSmICboxrRGS2iExzl9uA83Bq+kSFwd7jvsbd/rDWUDH7h1IMJtPMbM8n4LUaTWUI3j7/63hMcjI0Fccy96JQvAeAZ9m2BOqeRLlQPnXW4kxqvD+V3uMrlxyXWW6Ox3ISb6a1NXFSIFPTj1fmIONCcd0qczpa+MMTD488rpKYgBvjnbnAr0XkeeAp4EFVvZfoMNibgZnu+muAlbUc7K4+J5hrZohF6OGPDFdV7nhqM4PJ/I14x5JghVW/T7spLjlNGpIpdS1w50BP72OxcBdKPBYuYy1NMYaS6cwDI2ioT2lt4hy34JRI7nkL4dVq9yZhPb94qecZDfWZC24YFUJVnwdODlm/AccfHlw/AFxWg6GFstOdwMzvQhlZvn/tNr78s+fZtLuf//EHo3btV4VgWGTSV7EvJpI1IehtT8RHfOCeKyXKhdIU4UJpjscYSqUz5wlLpvHix5vjsdDytfloDwj4UEbAR1fRsBRMwA2jjvD6QM5qj7bA/RasVze8UOTKWBIUcH/WZSImOd8eRizwbB94PMKFEuUDb3ELXHkPiLDdvnLJccyfPimrwqDHnI7ovwHAZNc948WQe+8j+ICpJibghlEnrN9xgC/d/gxQwALPcqFUfVijJijgw1kV+2IhFrgSj8Uylf3S6WwXSG4YYYQLxRV6r+RrWCTIzMktrLwg+5vLlNYEX/3A8Vx8wogf+8g5udmX7S3O+fsHHeEeKsGFcsefnslhHa0F9yuECbhh1Amf+dHvMl/jZxQ5iemJeW1TjUojHfCBBy3w/sHcBJhgHHg8JnR2tLKzbyhn0tKrp7LqylNZs2kPq37rNI7whH5gyEvbL/4mXXrSSHXsdX93QWgiTiIeY0Z7cyaT1HsQFWOBn7a49P6XYdgkpmHUCX598ZdH/XkgbdwfxTGyWJ8KPjCc4nVfKvxf3rWWpG9WMxYjdAI2HpeMrzzlxnAfd3gHkFtS1hPm9x93GH9x4UiKu2cJD7jnL1a/g4W1muKxyFjzow+bwivb9gMjFnihioWVxATcMOqEqMmvYFOHMLfJWFngL765L6sCX5DP//gZvuumzYNTXMqfuJNOw1BII+CEP5HHjeH+iwuP4eNnLOT9x3Vm7RvtQnHWv/+bvwUKVwR831FOQlYpKfbHzO3g1e0HSKWVwWTKiWE3ATeMiUeU7zT4lbzWbu89/UPc+3y4SF/y3f/kv972TGhHHXD6YQbxF69KqTI4nOasI2fywlf+ILM+EYuR8vnAYyJMm9TMVz+wLKftWeQkZuCBWCgb8przjnLGVKKADwyneX1nP0PJdE1DCMEE3DDqhijfaVCI/JOCtRDzL9z2DJ//8TNsDVQNhBF3zsZd/TnbovCfJ51WBpNpWhLxTFz1RcvmZoURej7wIJ4eN0XEgc+ekh1FUsiF4t3/UgT86MOcLpOvbNtP74FBpk+KnruoBibghlEnRFlvibhkuUhCXShVGhPAm/scwT00lFsudYlbG/u1HX052yA7YiZzvqAFnkxl3vsLX/kDrr/8pKxysqm0hrqIPOGOssAPn5Yd5VHIhVKO9bxkdjsAm3YdZN2OPt4WEq1STUzADaNOiPKBJ2KS1d2l1i4UL8U8zDfsFYQKa38Wxc6+kdYBqYwF7kjR5JYECTetPZnJxNRQkfaiT6JS6edMCQp4/nG1RDRGzsek5gTTJzWxZc8hNvT2RzZ7qBYm4IZRJ0S5UBLxWFYzguw4wuqHESZcSzes3rVn1XoRGE++vpunNo50pZGQ7waHfOdJuz7wsOSctDoullSgEJV/H//vqO2ZsRS4SeVGj8yb3sYzb+zh0HCKxbMmlXWOcjEBN4w6IdKFErDAa10P3LN0D4akmnsa6SXnfPh7j3PZjY9ntoe5UPy1T+5fu41t+wdyrF+/1Z/WcPeHF30SFYWSO9YCLpSIzvaFWDhjEq9sc7r7LJzZXtY5ysUE3DDqBE/EgjrjlVf1yMrErMG4PEt2wOcmOTSU4qZHNmRE8Xu/fa3oyb+wuO+gCHuheKm0ulEo0eMqNkGnoAulzAiSozo7MstHzKitBV4wE1NEWoHfAi3u/neq6l+LyGLgdpxKbk8DV6pq/RZkMIw6xxOxv/WVOQWnR6TfhRLiQQl1VVRsXK4LxW+Bf+OXr3LTo69nXm/adZB7ntuac2wYj6zbmbMu6D4ascDTpCN84F4Rq3yPjeZELOPeKWSBB8vJFssxc6dkloPt36pNMY+cUpvCGoZRBqrQ0ZrgyjMXZa2PxSRLwMIM3Wr6wL1rH/RFoQRreAO8sSs3zLDYB0uwoqB3ze88vJ5UOlx8D3cbJuS7wm1/csbIWAoMpdyuOcfMHbHAi3XnVIqCVyujKaxhGGWQSodbmpAtYMF64FDdMELPB+53oYQJam/fQM66MB94GDkuFPc+rPrtBlSVsFDvG688lX/80AkZIQ/j1COmZ5oyFLLAPT7StaCo/Tzm19jq9lNUMSsRieO4SY4E/pn8TWGDxy4HlgN0dnbS09MTeo2+vr7IbZVmxbLceNZS6WyrzHmqQdh9rOX9HS2NNNZKkopwFQBk6dsoHN+PvbaTnX1DXFJCx5hExgIfEfAwLdx5oDQPqlevG3L7WvqbNERFocya3MJlIWJ7x5+eyRxfEk+bOzlZjK9843UXFTd4HyLC/7r42Jq0UAtSlICrago4yW1NdRclNIVV1VXAKoCuri7t7u4O3a+np4eobZXmkyvvG/U5VixL8o219VnMceMV3Tnranl/R0sjjbWSeCnjYWRHoZQ/ifmx768GKEnAPeHzYr037erP+JX9PPDitpLG0pIYEfCcCoMBl1G8BPdGsNJfvsqOleLqdy2u+jXCKEmBVHWviGQ1hXWtcH9TWMMwyiCvCyViEtPzh1ej6/n+gWEmNcUz0SXJlNI/mOS9/9hT8FhVLTimhE+0c6NQfAIekYlZLNPbm4CR5hfjiWKiUGYDw654e01hv8ZIU9jbyW4KWxZrt+4ryzIu5yuPYdQjQVfBopmT2LjrIJD99d9vdaeCxbZHwcOvbGdmewsnLpiGqnLC3/ySPzp5XsZKHk6n+f32A0WdayiVm5zz8TMW8qMn3si89jd2CAp49qRt9IOtGGa49Um8bkfjiWIs8LnALa4fPAbcoar3ishLwO0i8lXgGUaawhqGUQbpgAV+zxfexX432iMoaB4hlVjL5lP/tgaA33/1Am5/yhHau57ZmvHtDic1k7BSCK9AlZ+vfmBZloD7rexEjgvF5wPP41oqhumuC2XPGAv4bX9yBp0F2rSVSkEBL7UprGEY5ZEKVN3raG2io9X5+p+IcKFUygL3GvMC/M3/e5Efrx4RWq96YDKd5tUiBXxgOMVLb+7PsrL9XHvB0TyxYRe/ftUpNxv0gQcfWKMR8O6j5vCV//cSf3DcYWWfoxKc+baZFT9nfc7CGcYEJCrjEKJD4Cplgfu75vjF289wStnQGy3gU1oTIw1+h9Pc9Mjrkft+7PSFPLVxT+Z1VBihd92IirFFsXhW+7h1tVoqvTGuEZEFIvJrEXlJRF4UkS+662eIyIMiss79Pd1dLyLybRFZLyLPi8gptRprvknMSBdKhboa9x4YLLhPMpVmk+uTD8NfDGpgOJXV+i1ISyKeNTGZiEVPYqbSWlIUykSi4S3wRRUICTTGNUlghar+TkSmAE+LyIPAJ4GHVPU6EVkJrAT+HLgAWOr+nA7c4P6uOlHxzhCYxAxxoQQ7vxcindasyJb+kFrfQZJpZc/BaD+yX3SdIlTRY2qKS9b7aE5E+8CT6XRVomzGA2aBG+MaVX1LVX/nLh8AXsZJOrsUJ4MYsjOJLwV+6GYgP4ETLju3FmMNTmL6icrE9FwopXSRASeixI/fBx7FgYFkpvN6GMGJx3xDcgTZ350+Ogol3zeTiY4JuDFhEJFFOBPyq4FOVX3L3bQN8DrlzgM2+w4LzTIWkeUiskZE1vT25vZ9LIf8mZjhtVA8K7dUCzzpm1zccWCA34T0rgzib8QQ5MJl2ROEac1vgUP2e8qJQglY86bf4ZiAGxMCEZkM/Az4kqru929Tx1lbkgKq6ipV7VLVrtmzZ1dkjE7rsNJcKJ4Ql2yB+2Y/P3jDY9y/tnAWpSfgwczGS086nH+54tQsn3aqgAsFsicug0Wv/O83mRpdFMp4xgTcGPeISBOOeN+qqj93V2/3XCPu7x3u+q2Av8BGzbKM06pEdAfLbqnmE0ZPJEuNRhnyHbB5d24VwTA8AZ8TaBbsjc2vsWlVCkU45uuA449CSY4yDnw8YwJujGvEMWlvBl5W1X/ybboHJ4MYsjOJ7wE+4UajnAHs87laqko6XVx7ML9lmyxzEjMqPjuKREwYcFuhHTEzu2mB1yvTb0W/1tvP4xt25Zznrs++kxs/7gT25Cu9mu1PT5sPPAITcGO8cxZwJXC2iDzr/lwIXAecJyLrgHPd1wD3AxuA9cD3gc/WaqD5olBiAYs0c0yZk5g/cJsxPPBCcQWojnS7rc+f3sbbO6dkbfOKXPmH/uU7nw89z8kLp3P+8c6ccFMiWpSDPnAzwMNp+DBCw8iHqj5KdLnsc0L2V+BzVR1UBOm0RjY29rtW0j6x9pZLjQe/+dHX+V8XH8sNv3mtqP29jNCzj56T4/pIuk+RUjU2nwUe9IGbBR6OWeCGUScUG4Xit8A96zcdYYGn0sq/9Kznzb25fu5rf742pxOOhz+1fUpLgi17nASerkUzch4yGRdKiWayX8AXBbq5JwJhhOYDD8cE3DDqhHz1wGMR9cCf37IXiHah3PLYRr7+wKv83yc25Wy77ck3skqsvtNXq8MrRHXrp0/nmb86j4Pug2LGpOYcAfdqg5dugTtHfOHsI5k7NbsZQjwrlT5tAh6BCbhh1An5LPAsn7A7Adk/mMyUm42axLx/rTP/On1SU+j232/vyyyv+kRXZtnr0N4Uj5GIxzLdeDraEjkCnglJLFFjPQs87LDcaoSlnXuiYAJuGHVCVPNeyG4x5ol1ny97MmoO86W3nJD3YqJOJreMTIl5Au49ODwru6O1KccH7p07rEtPPjwBHwoZW3AS03zg4ZiAG0ad4KTSh2/zT2J6Pmd/j8ooF4pnmHsp8EtmtRc1Fi/qpSmQ4t7R1hRpgW/ZU1w8ucfHTlvIu46cxafOWpSzzXzgxWECbhh1Qj4XSiwgaAAHfQWoogTcs9Y9kS0ULz7FtcI9vQyOZ0prIscC95KCTpw/Ne+5g0xvb+ZHnz6dOR2tOdtyfOBmgYdiAm4YdcAPHn2d9Tv6impq7In1IZ8FPhSRiunptefeKBQu/qsV7+X25WdkknKCjRaa4rFIC/z/fvp0bvlUZXq8mA+8OCwO3DDqgL+99yWguExMT8D7XQGf0pJgcDgVelwqYIEXSvjp7Gils6M1I5jedX/6mTN5bvNegMgolI7Wpkz7tdESDJu0euDhFLTASy2IbxhG+UxqDrepwlwoh1wXyrT2psgyr96+nshGNVkIWtrBmO53LJrBp9+9BMitYeKfIK3UZGMiln88hkMxLhSvIP6xwBnA50TkWJwC+A+p6lLgIfe1YRgBVJWnN+3J26HGo6MtXMCzXCiaPYk5Y1IzAyEWuD+5Zyg14kLxQgo7Wkeude8X3l1wbB6eBT53quO7PsqXWl8pSzno886TtDmhKXhbyiiIbxiGj3uee5MP3vAYdz/7ZsF9vZT1IGEuFE/Ap01qDrXA/en1ngWeUuX84w9j43UXZcrCzpnSwlGHZdc38XQ47JHjZX/On97GTz9zJjdeeWpm22h6V+bDolDCKel2F1kQ3zAMH5t3O8k2r24v3NE9qsRqmIB7k5gz2iMscJ+A7+ob4tbVm7KyPb0koB0h/TC/ffnJXHD8YRwxY1LOtsPcqJEPnDyPdyyawdS2kYdOdEeh0NV5+dP3LBk53mYxQyl6EjNYEF+y6xOriIR+PxSR5cBygM7OTnp6ekLP39kGK5YVbutUL9TzeMPucV9fX+S9rzcaaazF4KWlRyW6+F0rUYZmqIC7oj21LdwH7q/H/fiGXZnyrsVYs8fPm8oNHz81dNuS2ZN59q/OY9qk5pxtUS6UcizoZb6wRNPvcIoS8HwF8VX1rUBB/CxUdRWwCqCrq0u7u7tDr/GdW+/mG2sbJyhmxbJk3Y534xXdOet6enqIuvf1RiONtRhamtyMwwgBP+SznoupheIJ+HAqjYiTQel1gZcQX3nuuUobfxhh4g3RlnI5Au4/xqJQwimoQEUUxL+O7IL4xhizaOV9OetWLEvyyZD1fjZed1G1hjSh8dwig8nwUD9/SnywXZmH37PijyxpjsdobYqRVifczh9NEhUy6In8pScdXpRfvhQiLfAyfOP+Z4FFoYRTzG0ttSC+YRg+mjICHm6B3/n0FgDOWDKDS048PHSfsCiUoZQj4J6LJugHj4p68dwxf3vJ8cW+haLxW+D/5eSRXtDlWNB+0bZaKOEUtMBLLYhvGEY2nuAODucKuKry9QdeBeCPz1oc6YLwF7NK+opHNSccCxycB4Q/liTKAvcu0dYcL+l9FIN/+P6CVOVMQvpdKKbf4Vh0pWFUGU9ww1wofpH1VwMM4hdDf32T5kS0BR70gXvuGU8Yo7r/jAa/pexfLscH7j/ColDCMQE3jCrjNR4eCLHA/d11JuWxiP0CmPT5wJviscwkadBF40WhXLRsLl//4Am0uoJdTTGMRbg9ynGB+P3mFgcejgm4YVQZL9VcQ9JiirXA/ZOY6UwUiua1wD1L/d1LZ/HhdyzIWNzVNGb9Qu0vSFXONcWiUApiAm4YVSYZUSkQst0c7XkEPMwCH3Qt8NYIC9x7OHgWtyf01bRm4xEW+GjDCM2DEk59BjIbxjgiGVWrO6389ve9mdftEYWsIFsM0+nifOCeBR4P+Lz9wvj1D57A7CktRb+XQsTMB15TzAI3xj0i8gMR2SEiL/jWhVbTFIdvi8h6EXleRE4Z7fW9Uq7BqL5bn3yDz//4mczr9pZoH3hYV3onDlyyLHB/0SzPAveODRPwD79jAe87ek5Z76vQWOMx4fFrzwbgU+9aVPJ5si1wE/AwTMCNicC/AecH1kVV07wAWOr+LAduGO3FvSiUYDecLXsOZr1O5Cm5FwuJAw9a4Pc//xYfvOExbl39hns9Z3/vUK9Ea7WNWc/iT8SEuVPb2HjdRSx/z9tKPo9/nBYHHo4JuDHuUdXfArsDq6OqaV4K/FAdngCmuaUiysazwIONhaMKV4URVgvFS+TxLPC1W/cBI8WzMi4Ur79lvPpRKDDywBit1eyfxDQDPBwTcGOiElVNcx6w2bffFndd2XjCnUxnTzI2+QT8/1x2Yt5zBAVcVX1hhI4Fvu/QMACt7uuMC0U8Afcs8OqqofeYCjZlKJUsC9wUPBQTcGPCo47TuHC3BR8islxE1ojImt7e3rz7esKdDFrgvkSaQsZ4UMDS6lrgiVgmvnvvwXAB9yxuz0VTdW+E+zbj8cpZ4OZCCccE3JiobPdcI4FqmluBBb795rvrslDVVarapapds2fPznshzwIfTkVb4PEC1Z5OWzwDgNPd38l02vGB+yxwr6rhT9dsRlUzk6aZKBT3etUWQ89HX0kL3IpZhWNhhEaGsCqGhWjgCoZR1TTvAT4vIrcDpwP7fK6WsvDiwN/cO8ATG3ZxxpKZAPgN1EJit2DGJDZedxHf+81rrH59N8mUjtRCCaTEb9jZz+rXd2csce/Z4LlQqi2GnuW/YHpuM4hSyE7kGdWpxi1mgRvjHhG5DXgcOEpEtojI1URX07wf2ACsB74PfHa010/6GjBcvuqJnPVQvF/ac7v0DyXZ1TfEjPZmEvFYjlU9lEyPuFCkxi4Ul6PndozqeP84LQ48HLPAjXGPqn40YlNONU3XH/65Sl4/6Drx8At4se4GL2TwW79aRzKtvN1tKNyaiNE/NJLIE49JThRKe7OX8BOdGVoJVl15Knc+vSW0HVspWBx4YUzADaPKBCcvPYZ9qe/F+qU9C/zHbqz3sYc7Vu6klkSWgMdEMhmbng/8xAXT4PFNPL9lb0njL5X3H3cY7z/usFGfx6/ZJuDhmAvFMKpMMHxQNXdSs1QBB1gyqz1jgS8MWLsiI5OJni/5/ccdxqlHTOfPuktPqhkLsisbjuFA6hi7LYZRZYIJPCOJOOW4UEY+su9eOiuzvGhme9Z+6bRmysl6D4fJLQl+9mfv5IT504oe+1giFoVSEHOhGEaVCVrgybSSiGdXKSx2ks5vgU/1NRZeMjtbwIdSabbvHwAa13q1psaFadA/rWE0DlEWuN+FUo4FPq2tKbMctMCTKeXPf7YWaFz/cdYkpilVKAVvSymV3AzDyCVYDzwZ4kIp1gL3C3i/r5v9ghltWfv5Hw6NKuA2iVmYYp5r/0bxldwMwwgQZYEny7LAR0rOnnNMZ2b5bbMnZ+33+dtGytQ2ahp6zAS8IAUFvMRKboZhBAjGgXs+8dFGoXghhOB08/FnxabKSBKqN6wWSmHKncSMquSWg4gsx6mrTGdnJz09PeEnbIMVy5Kh2+oRG69D1N9zNPT19VXlvGNFsCNPytfT0qNYgWopo5N8o4qftVQrzKijUFRVRSSykpuqrgJWAXR1dWl3d3foft+59W6+sbZxgmJWLEvaeIGNV3RX/Jw9PT1E/Z80Ijk+8NRIPW+PYl0ozWUIeIMa4OZCKYJy53ajKrkZhuFj+/4BNu46mBWznUortz/5Bg++tD2zruhaKG5MoNfEIciU1tyH9KGhVMie9Y/gt8BNwMMoV8C9Sm6QXcnNMAwfp//9QwAcPrWNr31wGeC4VFb+fG3Wfoki4+SaXAs8GDbo8cS1OeVdImux1Dt+zW5UN1C1Kfid2q3k1g3MEpEtwF/jVG67w63qtgn4cDUHaRiNSDBRZ3KLE7edCulSX2ycc0drE9/8yImc9bZZodsTIXVXT1nYmFG+/tBKM8DDKSjgpVRyMwxjhP7BEddFKp3OWJHBzEzI7Vifjz86eX7ktqbAk+ALZx/ZsKVYW7M6FjXme6g2lt9kGFWib2gk6ieZ1sxEZZgFHrauHIJi3dSoefQ4oZEe5gMPp3H/uoZR5/gzJZMpzfSIDBPrDl9afCUJc6k0Cv6QSRPwcBonDs4wGow+v4Cn0xkLfMhXB/xTZy3mM91LmNHenHN8JWhuYAtcLA68II371zWMOsdvgQ+nNOPH3bT7IAAfPGU+Ky84mjlTWit63S+duzSz3MguFD9+d4oxwvj46xpGHZLtQklnQgVfeesAAB87fWFZiTmF+NK5b+eco+cAje1C8TNzcnW+oTQ6JuCGUSUODGRPYnoW+IadfQAsmjm6npH5aHP7XwajUhqV6ZNMwMMYH39dw6hD5k+fxKzJLYAzien5wHf2DSIC06ooSu3NjsshWIelUWltihfeaQJiAm4YVeLMt83kux87GXAiTzwLfFffEB2tTVWNbZ7U4gjewaHGKbhmlI7NDBijYtHK+8o6zl/6dDxzWIczQXnigqkZf/TOvkHmTWvLd9io8Szwgw1aB8Xjv56zlC17Do71MOoWE3DDCEFEzgeuB+LATap6XTnnWTSrnQe+9G7eNnsym3b1A05EytQq+3Q9H3h/g1vg15z39rEeQl1jLhTDCCAiceCfgQuAY4GPisix5Z7v6MM6aIrHiPsmFKdPqk7ijsdlXfM5Zm4HV55xRFWvY4wtZoEbY0I+18uKZUk+GbG9Rq6X04D1qroBQERux+lC9dJoTuqv+X3UYVNGc6qCzJnSyi+++O6qXsMYe0zADSOXecBm3+stwOn+HfydphYuXFjUSQ+f1sYnzjyCVFr57HuPrNBQjYmMCbhhlEGw01Qxx8Rjwt9eenxVx2VMLMwHbhi5bAUW+F7Pd9cZRl1hAm4YuTwFLBWRxSLSDFyO04XKMOoKc6EYRgBVTYrI54H/wAkj/IGqvjjGwzKMHEzADSMEVb0fuH+sx2EY+TAXimEYRoMyKgEXkfNF5FURWS8iKys1KMMwDKMwZQt4pbPVDMMwjNIYjQWeyVZT1SHAy1YzDMMwaoCollcvWEQ+BJyvqp92X18JnK6qnw/sl8lYA44CXo045SxgZ1mDGRtsvNWj3LEeoaqzKz2YQohIL7ApYvNEuO9jQaOMtVLjDP3frnoUij9jLR8iskZVu6o9nkph460ejTRWgHwPjUZ6LzbWylPtcY7GhWLZaoZhGGPIaATcstUMwzDGkLJdKFXIVivoZqkzbLzVo5HGWohGei821spT1XGWPYlpGIZhjC2WiWkYhtGgmIAbhmE0KGMu4PWYji8iC0Tk1yLykoi8KCJfdNfPEJEHRWSd+3u6u15E5Nvue3heRE4Zo3HHReQZEbnXfb1YRFa74/qJO9mMiLS4r9e72xeNwVinicidIvKKiLwsImfW+/0tlXr73xaRH4jIDhF5wbeu7u55I33+RKRVRJ4UkefcsX7FXV+bz56qjtkPzuTna8ASoBl4Djh2LMfkjmsucIq7PAX4PU65gK8DK931K4GvucsXAr8ABDgDWD1G474G+DFwr/v6DuByd/lG4M/c5c8CN7rLlwM/GYOx3gJ82l1uBqbV+/0t8f3V3f828B7gFOAF37q6u+eN9PlzrznZXW4CVrtjqMlnb6z/yc8E/sP3+lrg2rEcU8Q47wbOw8kinev7J3vVXf4e8FHf/pn9ajjG+cBDwNnAve4/1k4gEbzXOJFDZ7rLCXc/qeFYpwKvB69Zz/e3jPdYl//bwKKAgNf9PW+Ez5973UnA73D6p9bkszfWLpSw5rHzxmgsobhfcU7GebJ2qupb7qZtQKe7XA/v41vAl4G0+3omsFdVkyFjyozX3b7P3b9WLAZ6gX91XT43iUg79X1/S6VRxlzX97wRPn+u6/JZYAfwIM43r5p89sZawOsaEZkM/Az4kqru929T5xFaFzGYInIxsENVnx7rsRRJAuer/A2qejLQj/OVOEM93d+JQr3d80b5/KlqSlVPwvkWfBpwdK2uPdYCXrfp+CLShPPPc6uq/txdvV1E5rrb5+I8cWHs38dZwCUishGnKuTZwPXANBHxkrX8Y8qM190+FdhVw/FuAbao6mr39Z04gl6v97ccGmXMdXnPG+zzB4Cq7gV+jeMyqclnb6wFvC7T8UVEgJuBl1X1n3yb7gGucpevwvHNees/4c6GnwHs833Vqzqqeq2qzlfVRTj38GFVvQLnn+lDEeP13seH3P1rZs2o6jZgs4gc5a46B3iJOr2/ZVKX/9sh1N09b6TPn4jMFpFp7nIbjq/+ZWr12RuLSYmA4/9CnFnm14C/HOvxuGN6F87Xs+eBZ92fC3F8VQ8B64BfATPc/QWnucVrwFqgawzH3s1IFMoS4ElgPfBToMVd3+q+Xu9uXzIG4zwJWOPe438HpjfC/S3xPdbV/zZwG/AWMIzzLejqerznjfT5A04AnnHH+gLwV+76mnz2LJXeMAyjQRlrF4phGIZRJibghmEYDYoJuGEYRoNiAm4YhtGgmIAbhmE0KCbghmEYDYoJuGEYRoPy/wHKm0plAQDTOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 134\n",
      "n_zeros =  3\n",
      "mean =  246.39906359335095\n",
      "var =  15660.75230972451\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 150\n",
      "n_zeros =  137\n",
      "mean =  15.765005393369908\n",
      "var =  181.34843744219546\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 161\n",
      "n_zeros =  2\n",
      "mean =  25.336321925572314\n",
      "var =  144.0213077339358\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 162\n",
      "n_zeros =  10\n",
      "mean =  12.030884836589687\n",
      "var =  66.8756241174203\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 163\n",
      "n_zeros =  3\n",
      "mean =  53.61600978877659\n",
      "var =  1864.603175319041\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 196\n",
      "n_zeros =  22\n",
      "mean =  207.1033576888568\n",
      "var =  18771.21524643706\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 204\n",
      "n_zeros =  307\n",
      "mean =  0.009969425043076923\n",
      "var =  0.002763872949324695\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 207\n",
      "n_zeros =  297\n",
      "mean =  0.054273643899999995\n",
      "var =  0.08721836851856082\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 215\n",
      "n_zeros =  308\n",
      "mean =  37.22107555369232\n",
      "var =  52766.585570909396\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 222\n",
      "n_zeros =  54\n",
      "mean =  23.383600833464612\n",
      "var =  252.3939022129706\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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lxv3DDh7p47N3reRji5/lwOHe+HYvFj9iWC1jRtQzurE+5RzevSn30Ix57oaRytnAxSIyH2gERuOUt24WkTrXe58KbI3QxpzwwjKVqO29/bH433e4L8Yrb+0HYNu+wzS5It7ZdQSAcSMjGQsvKua5G0YSqnqTqk5V1TbgcuAPqnolsBS41N0tstIa+TSXiIt7hbjuf1zbSduiRzn+y4/x2xcGnrWHevrj8fStew/F1/9l+wGaGuqYNLox9Jze/S1zx93E3TCy4EvAF0VkHU4M/o6I7cmaeFgmYjsKxeNrdgDOWMI/3v9SfP3h3n4a6msB+MYjr8TXr915gBNaR6V9uA2EZcpb3i0sYxhpUNUOoMN9vQGYG6U9fnKRngHPvbC2REVjfbB/+ms3JRKcKpAeu7t6OH7iqLTnjA+o5m3dAKs27+UXz2ziO5eeRk1yR+4hwjx3wygz8hHmgQHVylD3Rtc7T8fBnn7O+24HBw73sudgL80j0sfbh2JA9ZqfPceDK7ey52BP6D7PrN/Nlj3hs2uzxTx3w6givEmtlSHtmYk7wIbObtbt7GLfoR6aR6RmyAw13nOiJs1D9Yr/+yx1NcK6b84vyDXNczeMKqLSwjLpxBJgzPABIe/tV3r7lebh6cXd+1ZTyJrumc4MLuQ8BBN3wygz8gkbDAyoVoa6H+nrT7u9dXRD/PWGTqe079hBwjIehQzLeOcqZm0fE3fDKDMKkwpZKGuKS/eRPhb+6E+8tGUvAEfcEr7/kVS212Ni00DK4/ee+AsA40dlFnPPh4dWbeW+5zfHlz1JL2ZtHxN3wyhTcgkb9JdJKuQ3H3uV5ze9nbJ+zVv7eXHzXhY9sBpwUh6bGuqYP2syG2+Zzx9vPDdh//ragb/Ue7C97x3pZw3H89zz0OHr71nFjQ8MpGZ6aZXFLP9g4m4YZUpOYZkymMQUiymLn9rAR29/JmWbJ9Cb9xxky56D7DvUG89nFxGOHjeCn1/zzvj+08aNiL8WgRMmjqKutviy52l6WOG2ocipN3E3jCqiHMoPHE4TR/fqxBw43Mc531rKgyu3ssstKeAxb8ZELnKrPs45ZixfX3iyc97eGLUZ5JjHxzQKOKDqnSsWIuJDUa3TxN0wyo0C5LmXsrof6kkn7tk32zj5qNGAM/han4HXPlAVMutLhaJJnvvOA4f5y44D8e1DEa6xPHfDqCLKIc/9UG+4uO/3VXhMx8hhjrTV19ZQW+MIem+/Zum5F474gKqr8uf+WwfdPf1sunUBf1zbyeqt+wp4NQcTd8MoU3IqP1DiM1QP9vQVxHP/8vyTaGlq4IMzW3lt+4CH7B9gDWNgQDXzO7xz/2HmfnMJ/3nVGXzo5Ekp25MHVLt9f+NVdzyX8XWywcIyhlFm5CPLsRJOhdyx/zAzb/49P+5YH7rP/kOpnvsPrzg9Zd2YEfX8w4dmUFdbkzDRqa5maCTv5bccz/vu594M3D7YgOpQYOJuGFVEKQ+obn7bqavyoK90733Pb6Zt0aO8tfcQ3/7da7zui1N7zG0bl/a8dT5vvS4Tzz2HsExskHCX57nHYsHrhwILyxhGmZJTD9USCcsc7Omjt08Z46vzEuTV/vvSdQB8+Ter6Xi9M/BcIxrS15fxx9nrsqjI6N1eVeWnf9rEJbOPYvyohuB93d/J5RBiMaWmRgY896T/WW//0Im7ee6GUaaEpdWlPaZEPPdzv9PBaV9/PGGdN9vUjze46i/bm4w3eBqGX9AzyXGXJNf95a37+cYjryTUi0/G+18kl/Pt6U/8m5IfYOnSPvPFPPcIaVv0aE7Hbbp1QYEtMcoJT3z6Y/DbF7ay4NTJGaX4wUDsN2p27Hdy09sWPcrzX3k/LU0NdB9JHSz1Blf9g6KTRjeyff+A2A+WAZOt5z5Qz925WV79mt3d4eV6NWTmb09/LKFyZYq4p8kMyhfz3A0jCRFpFJHnRORFEVkjIl9z108XkWUisk5E7hWRSBpxegLy8Itv8fl7V3F7mgHIZEqxQfam3d3AQPNqP0HrWseEt8gLojZLzz2ZoG8UyXhfopLDMr196T33I72p517z1j7WBowtZIuJu2GkcgQ4T1VPA2YDF4jImcC3gO+r6vHAHuDa6EyEzgOO95s8QzMdpdw6zi/kH5kzJXS/Sb5Kj5l8i83ac0+quunP0Lns9mcSerV6eJqd/NBMDsskh9Je2Lw35VwLfvg0H/j+U4PaORgm7oaRhDp0uYv17o8C5wH3u+vvBC4pvnUDAtLvpl5kMzhaKmGZILp8Oex/O++4+OvZRzfzwGfeHV9O19w6CH/6YzZhma/8djUbd3WzzY33H+7p57lNb/P5e1elHPP9J52Kk8mee0+S5/7n9bt4dsPu+PLn7n4hkz8hJ0zcDSMAEakVkVXATuAJYD2wV1U9BdoCBLqXInKdiCwXkeWdncEZHoXAmxAzWMMKP7kMwhaLAz7PffqEUVx7znQApowdzhnHjOWS2UcBMLl5eFbnzTYs4z0sH1u9nc/81wq+7jbYDou59/XHWLezyz02cVtPX4zfr9keX/7R0vVcvvjZrOzPlbzF3f0QvCAij7jLJRGXNIx8UNV+VZ0NTMVpin1iFscuVtV2VW1vaUlfXjYfcqnNXgqee7I36z1vdiQNko5scPI9vM5J3770NB793DlMbApORwwj17AMJMbb/eGv9Z1d8df+0Evyg/ZIX4zP37MqG3Pj5DvhqRCe+/XAq77lkopLGkY+qOpeYClwFtAsIl6G2VQgNfhaRPr6Pc8982MGYu7Rjah+9q6VCct9bnhp277DDKut4Wduyd7hbpaJl20yrK6Gk48aw7C67GQrMRUy87AMENrQ+vzv/j827epmxRt7mHnz7+Prk/8XPf2x+EMqW4778mNs23cop2MhT3EXkanAAuAn7rJQInFJw8gVEWkRkWb39XDgAzgOzFLgUne3TwIPRWKf+7s/h7BMMae/h/H4KzsSlr2JPNv3HWb+rEmcO2MiMDCmkCzmw7LMePF77pmmjHrsPegMpnohIj87Dxzhv198K2Fd8vhHT1+MMcNzzzj/9C9X5HxsvnnuPwBuBJrc5fFkEZcErgNobW2lo6Mj8AJdXV2h29Jxw6zsS4MORuvwoTlvtnR0dOR8X4aCCrRlMnCniNTiOED3qeojIvIKcI+I/AvwAnBHvhfKBy/mfuBIHy9t2cupU5sHPaYEtD2F3r4Yqsr2fYeZNGYgnu6Fb5IFOVvPvdYnuJlUhQyKc7UfM5Zrzm6Ld4B6et0unl63i5//eVPKof5Wej19sbzK+e7uCs+tH4ycxV1ELgJ2quoKEZmX7fGquhhYDNDe3q7z5gWfoqOjg7Bt6bg6xwlC6bhhVh/fXR39vK9NV87L+b4MBZVmi6q+BKRUo1LVDTjx95LA82zvWvYmdy17k4c/e/agAl+KA6q9/TF2d/fQ0x9jsi+H3SvV25QU1sjWc6+pEUSc2H59FtkyfkY11jF17Aj+61Pv4pn1u3l63S5+uGRt4PGX3v7n+OuevhiHevo5rmUk6zu7s7Ib8vumlY9SnQ1cLCLzgUZgNHAbblzS9d4jj0saRqXSl1SX5PlNewYV91LMc+/pj9H+L08CMMkn7p96z3S6e/q46qxjEvbP1nMHR7CVgQdG2n0D1H2U7wGTLgq2q6uHlW/ujS/39Mc41NvPKVPG5CTufcmVxrIg55i7qt6kqlNVtQ24HPiDql5JicQlDaNSiZcfSBLqWAZeXimGZfwZKf4c9pENdXx5/kkJ0/chN3H3yGxANXWfpsYBcU83xrFxV1fCcm9/jMO9/QkPB48ZrU0p65LJJ6QzFDGGL1FCcUnDqFSSP/h+sf+7u1bSMqqBf7745IR9ogzL9MeUO5Ni1AA3+gpy+Rtah5GLuHt/dSbNOoIYP3Ig/TKd57757cTsloM9/fT2a0LGzHc/ehrNI+o5dWoz7/zXJ9NeN/nbWTYURNxVtQPocF+XVFzSMCqN5GwZApYffWkbQIC4e6+KL/L3Pr85PiEoiCvmTmPsyMGnxWQbc4eBXPq28SMH3TdZvJ/6x3MT7Mom9XSfW7pglK8s8ciGWs4/qRVwxhMOBNTP8YgkLGMYRkTEyw+Ei3sY8aYRETjwYTnjHqMbM/M18wnLnHfixEH3Sdbu1jHJk6YyV3dP3P2e+5jhAw+KlkEmZEU1oGoYRhSEfN4zic/G4uJeHHVf89Y+FvzwaU4+avSgotxQn77phkcunvvfvGc6s6Y2Z1R+YG9SK7/k6+XmuQ9I7diRAw1KpowdzoZd4QOtvf2KqubUXMXE3TDKjDBZ7s/gK7yn/5kMvhaCZ9Y7RbLWvLV/0H0b6zMT7Vw8968smJnxvsm2JgtrNkLrNfQe4Wso0uzz3KeOHXyM4YXNe5kzbWzG1/SwsIxhlBlhXnd/BuFZ79hijauG2XrVmcekrGusy9BzzyMskwmffu+xabf7Pfd//fApKdv9g7ZeMw7/umZfa8GpYwcvgrYhhxRKMHE3jLIjzOnOxHOP9wUtoD3pCLP1Hz40I+dz5hKWyYZTpoxJu92fKjm3bRz//tenc9vlswe2S6q4+1Mw/amdmZQv9hdUywYTd8MocXZ1HWHG//mfeB3wvDx3V22LVWMmzNZhtTXce92Z3P7xOVwxdxqQeWZILt2UCok/KjN8WC0XnXoUC2cPVFnxh7y8TkvD6+s4cVITp09rTjjX5ICuUv5GJSOG1abtH5sOi7kbRonzl+0HONIX42v//QrvPm48r20Ljl9nE3MvlriHhX+G1dXwrmPHAwPdiHrzyOkeKj58emppLL+4N/hCSS1NDXQeOJIwsO01wK6tEX73+femnGuKLywzYdQwdnX1MKqhjsvap3Lf8i0cPXZEQr/YbDBxN4wSp8YN8r66bT+vhgg7pM5YDcLzpHvzyJ/OhrCHSFCN9Xwm7AwVC2ZNTllXE1KI7I83ngvAif/0u/g6LywTlmFzzPiRfPPDs5g6djgr39zDD55cS22NcMtHTuWfLprJk6/uSBiMzQYTd8MocTyBGIwgIe3tjyVUVVTfgGospvEHx1CRScrl2cdN4EdL1/POtuwzQoaaoMQY/zr/7UsukwAD30bSJdj89bucsJTXzelwbz+1NUJTYz0fPn1q9ka7mLgbRolzpC8zLztI3I/0JYq7f5feWIyGmswyVHIlk5TLdx8/gZe/9qHA+itRE1RHxr8uXVqkyEDZ4kzSJye6jb/fDmnnly2ldzcNw+DBlVs489jxTGxqyNhzD5rEdMQtWnWwp49htTUJnnQx4u6ZXiJbYf/0+47lxEmDF97KmyDP3fc63Refpoa6eAu+TBqqjHNLHOzp7h1kz8wwcTeMEuHZDbt5ees+rpg7jS/e92J8/aVnZPbVPMhLPux6jjNv/j3zZ01KmPpejAHMTMYBcuGmC08akvMmEyTKfi88nWg3NdbT3dPn7jf4tTxx33uoMJ67pUIaRhIicrSILBWRV0RkjYhc764fJyJPiMha93dBg8SXL36Wf3n01RRBvH/FloyOD/LcD/qKUj22entCPfeh9tzf7u7hUE9m3zpKlSBNToy5h6v26OH1A2GZDOrRHN8yioWzj+J7l83O0spgzHM3jFT6gBtUdaWINAErROQJ4GpgiareKiKLgEU4Ja4LSn+OHnXQ4GVXUsVB/z59GSTGx2JK+78+ydvdPVwx92hu+cipGdsz5xtPZLxvqRKk3Ykx9/Bjh9fX0BsPywx+rbraGm67PKUBWM6Y524YSajqNlVd6b4+gNMcewqwEKfpOwxh8/dc0xS9VEK/d36wpz8hXON31jMpNNYbi8UH+O5+bnNGdqzv7IrXlCl3AsMy/tdpRLuutsaXLTO0WUmB1y/6FQ2jjBCRNpx+qsuAVlXd5m7aDrSGHBNv/j5t2rSsr5lrvncsoJxv15G+hIdFouc++HUGC938zS+WM2l0I19ZcBJffWgNf3XGVC77z2eytLx0CZLkmkFi7t+45BQeX7M94V4PccZpIOa5G0YIIjIKeAD4vKomzB5Sxz0OVD5VXayq7ara3tLSkvV1cxX3voDSAt1H+hKWNcFzD/6G8MtnNrH09Z1A8KDrwZ4+7nt+MwcO9/LEKzv45bNv8NM/beTe5ZsrStgh2OMeLOZ+1ZnH8Mtr35XQr3Wo5xMEYZ67YQQgIvU4wv4rVX3QXb1DRCar6jYRmQzsHIpr9/QHD0IOq62Jp9YF0R8i7n0JYRmf5x7ilf/TQ2sA2HTrgkDP/Q+v7eTGB17il8++EV/37d+9HmpXOZPNJKZk6n0bI3DczXM3jGTEcdfuAF5V1e/5Nj2M0/QdCtz8vds38Hm4d0DA/2rOVCaNbmTM8Hoe+My7uebsttBzxMXdJ+APrNzKT57aEF9OiLln8A0hyLv3YvCv7zgw6PF+Rgyrpamxjk+/L31J3VJisFTIdLF0f2kCi7kbRmlwNnAVsFpEVrnrvgzcCtwnItcCbwCXFeqCr20fEMpDvklLLU0NPPvl8+PLs6aO4Wd/2hR4jqCwzKrNe1nlFuaCZM89VbiTy8smPwAO9vSxq6vH3ZbdwO8rX78gq/1LgeBsmcyO9c8MjiLmbuJuGEmo6tOEf5M+P2R9XvgLgvlzw4MaU9z+8Tk8tXYXdy17M2G9lxWTbsq/P5PmJ3/cyPcuOy1eQtdrieffNzksM/Pm3w9cr/TqfBWcIFHOJGcdEmu4ZzJDtdBYWMYwSoCdPo/ZX26gIUDcLzhlMheeMillfV9AWCYZv7P+8Itvcc/zA+mNS15NHEKYftNj8Tzt6iWotkxmR/rDMibuhlGl7Dk4UE/EH5YJ6zrUENCSzgu5eN72WF87t+R9PPwPkq17DqXsn0uJguEZNrouBwKFPNOwjC9bJgJtN3E3jCg43NvPHU9vjAvx2wcH6okc8Q2ohvULDWom7cXHvXOOH9WQsk9yKMV//qCaJru7joT9CQl84qxj4n1CT5/WzA8+Njuj40qdoIHQTL3w2lr/gGrBTMoYE3fDiID/6FjPNx55hQdWOnVj9vjKvB4aJCzjrE/1jvtjyta9h3j0JWee1bgRw1L20STP3f/NIKjUbKZdgFpHN3LWcRMAp8zwJadPYdOtC9jwzfkZHV+q5OG4J6RCRhGWsQFVw4iA/Yd647/3Hexl466BDvcJYZkQcfcP1nm8vuMAZ9/6h/hyc0hYprZG4t69P9d9d4C479ifmedeVyNc/e5jeOovnbzuy/yJYvJOIRmsnns66hKyZUzcDaMq8AbbYqrM/sbjqEJTYx0HDvclxMHDxL02A7EYG+C5L329k+H1tRyKOdf4P799mU27uvmHD81IeMB4bN+XGoevEac8rZcSCY6QnTFtHJBarOzfLj2VkSXYiCMTBpvElI66BM+9QAZlQXneccMoczxx748NlARoaWrgwOE+dh4Y8JabGlO9b4DhwwYftBzRELxPfa1wyNcP4idPb+QnT28E4LwTJ9LTF+PpdbsAeHbD24AjTjGF41pG8uQX30fngSPM/eaS+DnqaoQx7jeFK+Ym1tP5aPvRg9paqgSLe/apkBZzN4wqwfuw+7NXJjY5A6D+/PWguDk4Me4f/fWcuJCefNTolH2C4vIAw0LWA/zHlXP423nHxZe9Waj/fPHJiMCn3nMsIsLE0Y1svGU+F592FDAgZBtvmc8tH5kVev5yIyinPVOhrk3IlrFUSMOoCrywin+AMyiMEhQ391hw6mRa3b6bQeGbU6aM5oMzgwpXJg6qzpnW7JyjtobG+tqQc41h4y0LErxyEYkLnReCiELEhpKaAIXMNH4e9YCqibthRID3YffnkdcF5LSPHRnsuXt4U9yDxKOhrpbFn2jnuJaRCeunTxjJJ846Jr587oyJzv5uemWQuNeFBI29Z1NdkApWAIGee4bH1tZGG3PP+T8SVSsyw6gEvOqOB3sGBh+bGlOHwEYOElsfFhf31G3esyL5G0FdTQ1fX3hKfLnZfYB4GTTeA8P/vBhMvGvLPCsmjKA/K3PPPdpsmXwet14rspnAmcDfichMnNZjS1T1BGCJu2wYhg8vI8bLOBk7op4Pnz4lZb/BwhzexKF0KXteaOfYCY4H72nOzRfN5MdXzonH9b20yEDPPSD1EgYCPBUWjYmTT7ZMWKZTscj56lG3IjOMcsYT9+37nElC//ihE+ntS6zj8vSXzh30PPV14WGZY8Y7Yt7sivdJ7qCrV1/mf58znQtnTWbSmEaAeDNn79uA/4xhYZlKZ7BmHenwi3sU+f4FebTk0orMMKoZr2b7G7ud3PIxw+vp9lWDPG3qGKaOHTHoeeIx94BP8nTXU28e7njuXognudTvKVMSM208UVLwDZhWZkx9MIJnqGYYlin3kr/Jrcj8TzpVVREJrDzk7zPZ2tpKR0dH4Pm7urpCt6Xjhll9g++UJa3Dh+a82dLR0ZHzfRkKzJbs8Tz3t1zP/R2tozh63ICYX/bOzHLDvXj6CROb+NO6gabU/hi4Nyg7Ypjzce9JKgbWUFfLV//XTKa51/eXJKivraGnL5YwOFhNBNeWyezYBM+93Gao5tOKTFUXA4sB2tvbdd68eYHX6OjoIGxbOq5e9GjWxwzGDbP6+O7q6Od9bbpyXs73ZSgwW7InuX7M9Akjqaut4RsLT+afHlrDsRNGZXSe806cyKOfO4cZrU38/M+b4usv800cGu+K+2jXgw9qsnHN2dPjrz1REtzWfn2xhLQ+P8m1aiqNwHruGQq1/yEZxaMxZ6XKoBXZrRS4FZlhFAMR+SlwEbBTVU9x140D7gXagE3AZaq6J9drdPpmoc6Y1BRPg/z4mcdwxjHjmBkwKSmI2hrh5KPGJKz7ww3vi3vhABfPPioh7DNYjXZ/WGZYXQ0cqbz89UwJCsFk6rn7i76V2yQmrxXZeSKyyv2ZjyPqHxCRtcD73WXDKCd+DiT3hCtoFpi/nd1JkwaEXEQyFvYwjm0ZlZAzP2JYHRfOmhwX7cF6p9bVCO9oHcUPPjY7np6ZXAe+Wsin/EBiWKZQFmVOzp57FK3IDKMYqOpTbpKAn4XAPPf1nUAH8KVczt/TF0toznG6O0N0qPE8yZ5BPHcR4fEvvA+A06Y28+ALW+OlEZJR3zGVSD5/VlnH3A2jisg4C8yfLDBt2rSU7Z1uA4wr5k5jT3cPlwTktw8l4waZ9eqnbcJIvviBdwy6X2VKe36iPCzikr/Vmd9kGHmgzihiaJxCVReraruqtre0tKRsH1Zbw2fmHcfHz5zG7VedQWOR2tKNcFMhL39n6gMnV86Y5kxA98f4K4lCee5RPP3MczeMzMgoCywTWpoa+NIFJxbQtMw45/gJPPa59+Qd0/dzzdltzJvRwrEtmWX3lBt5ee4Rx9zNczeMzPCywKBMs8AKMVgbdM5KFXbIz+G2sIxhlBgicjfwDDBDRLaIyLVYFlhVks9AsQ2oGkaJoapXhGyyLLAqI6+Yu38Sk8XcDcPIlZ9f806GF2lwtlrIKyxTZ+JuGEYBmOc23TAKR+EGVC3mbhiGUTLko8l1EbfZM8+9DGlb9Cg3zOrLujjaplsXDJFFhlGZ5DOg6j/WUiENwzBKiEI53FGUZzDP3Sg52nIo12zfSoyhIIpwSqEwz90wDCOE8pV2E3fDMIxQzHM3DMOoQMpY203cDcMwwjBxNwzDqECC2uyVCybuhmEYIZjnbhiGUYHYgKphGEYFUr7SbuJuGIYRShk77ibuhmEYYURRNqBQmLgbRhaIyAUi8rqIrBORRVHbYxhhmLgbRoaISC3wI+BCYCZwhYjMjNYqo5S5+t1tNI+oj+TaJu6GkTlzgXWqukFVe4B7gIUR22SUMP988cmsuvmDkVy75KtCrt66L+u65YYxREwBNvuWtwDvSt5JRK4DrgOYNm1acSwzCsr/XP8enlm/O3T79z92Gq1NjUW0KHtKXtwNo9xQ1cXAYoD29naN2BwjB06aPJqTJo8O3f7h06cW0ZrcsLCMYWTOVuBo3/JUd51hlBwm7oaROc8DJ4jIdBEZBlwOPByxTYYRiIVlDCNDVLVPRD4L/B6oBX6qqmsiNsswAjFxN4wsUNXHgMeitsMwBsPCMoZhGBXIkIi7zeIzDMOIloKLu83iMwzDiJ6h8NxtFp9hGEbEiGph51iIyKXABar6KXf5KuBdqvrZpP3is/iAGcDrIaecAOwqqJG5Y7YEU+q2HKOqLVEYIyKdwBshm0vpvqWjXOyE6rM19L0dWbaMfxZfOkRkuaq2F8GkQTFbgjFbwkn3UCk1W8MoFzvBbPUzFGEZm8VnGIYRMUMh7jaLzzAMI2IKHpYZgll8g4ZuiojZEozZkhvlYmu52Alma5yCD6gahmEY0WMzVA3DMCoQE3fDMIwKpGTFPcoSBiJytIgsFZFXRGSNiFzvrh8nIk+IyFr399gi2lQrIi+IyCPu8nQRWeben3vdweti2dIsIveLyGsi8qqInBXFvRGRL7j/n5dF5G4RaYzyvmRKqZXnEJGfishOEXnZty7w/ykOP3Rtf0lE5hTRzqw+lxHb2igiz4nIi66tX3PXB74/RaTBXV7nbm/L2whVLbkfnIHY9cCxwDDgRWBmEa8/GZjjvm4C/oJTSuHbwCJ3/SLgW0W06YvAXcAj7vJ9wOXu69uBzxTRljuBT7mvhwHNxb43OC3vNgLDfffj6ijvS4Z2R/reDrHpvcAc4GXfusD/JzAf+B9AgDOBZUW0M6vPZcS2CjDKfV0PLHNtCHx/An8L3O6+vhy4N28bonxTpbkxZwG/9y3fBNwUoT0PAR/AmUU72fdGe71I158KLAHOAx5x3zi7gLqg+zXEtoxxRVWS1hf13jDQz3QcTtbXI8CHorovWdhdUu9tnx1tSeIe+P8E/hO4Imi/CGxO+7ksFVuBEcBKnH67ge9PnOzCs9zXde5+ks91SzUsE9SIeEoUhrhfj07HefK2quo2d9N2oLVIZvwAuBGIucvjgb2q2ucuF/P+TAc6gZ+5YaKfiMhIinxvVHUr8B3gTWAbsA9YQXT3JVNK5r09CGH/z5KwP8PPZaS2uqHUVcBO4Amcb2xh78+4re72fTif85wpVXEvCURkFPAA8HlV3e/fps4jdsjzSEXkImCnqq4Y6mtlSB3OV/gfq+rpQDfOV+E4xbg3blx1Ic7D5ihgJHDBUF6zWinWez1TSuFzmQmq2q+qs3G+ec8FTizm9UtV3CMvYSAi9ThvoF+p6oPu6h0iMtndPhnniTzUnA1cLCKbcCpsngfcBjSLiDcJrZj3ZwuwRVWXucv344h9se/N+4GNqtqpqr3Agzj3Kqr7kimRv7czJOz/Gan9WX4uS+Jeq+peYClOGCbs/Rm31d0+Btidz3VLVdwjLWEgIgLcAbyqqt/zbXoY+KT7+pM4Mb8hRVVvUtWpqtqGcx/+oKpX4rxZLi2mLa4924HNIjLDXXU+8ArFvzdvAmeKyAj3/+XZEcl9yYJyKc8R9v98GPiEm4lyJrDPFxIZUnL4XEZpa4uINLuvh+OMDbxK+PvT/zdcivM5z+8bSBQDIRkOQszHGQ1fD3ylyNc+B+er3UvAKvdnPk4MbAmwFngSGFdku+YxkC1zLPAcsA74NdBQRDtmA8vd+/NbYGwU9wb4GvAa8DLwS6AhyvuShd2RvbdD7LkbZ9yiF+eb2bVh/0+cwfwfubavBtqLaGdWn8uIbT0VeMG19WXgZnd94PsTaHSX17nbj83XBis/YBiGUYGUaljGMAzDyAMTd8MwjArExN0wDKMCMXE3DMOoQEzcDcMwKhATd8MwjArExN0wDKMC+f9tdwbqZzjBVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 283\n",
      "n_zeros =  2\n",
      "mean =  25.8664547207877\n",
      "var =  181.4558347798876\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 298\n",
      "n_zeros =  214\n",
      "mean =  30.033897021831503\n",
      "var =  1746.4690655792976\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 331\n",
      "n_zeros =  134\n",
      "mean =  48.573767815027445\n",
      "var =  3604.6278771029647\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 345\n",
      "n_zeros =  34\n",
      "mean =  4844.079520534118\n",
      "var =  5549279.178282852\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 346\n",
      "n_zeros =  84\n",
      "mean =  180.35895886620858\n",
      "var =  63920.36618775997\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 347\n",
      "n_zeros =  14\n",
      "mean =  67193797.21922044\n",
      "var =  1486408801658898.8\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 350\n",
      "n_zeros =  2\n",
      "mean =  15211257.402864473\n",
      "var =  112073184767666.78\n",
      "====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column = 352\n",
      "n_zeros =  2\n",
      "mean =  17601419.038560092\n",
      "var =  137043660644789.86\n",
      "====================\n"
     ]
    }
   ],
   "source": [
    "columns = ops.columns[eq_0>=H]\n",
    "print(f\"many 0 n_columns = {len(columns)}\")\n",
    "for column in columns:\n",
    "#     plt.set\n",
    "    plt.subplot(1,2,1)\n",
    "    (ops[column].hist())\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.plot(ops[column].index, ops[column].tolist())\n",
    "    plt.show()\n",
    "    print(\"column =\", column)\n",
    "    print(\"n_zeros = \",np.count_nonzero(ops[column]==0))\n",
    "    print(\"mean = \",np.mean(ops[column]))\n",
    "    print(\"var = \",np.var(ops[column]))\n",
    "    print(\"=\"*20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "指定不同的空值处理策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 直接删除列\n",
    "delete_col_list = [12, 24, 25, 45, 46, 219, 214, 88, 95, 109, 150, 204, 207, 215, 222, 298, 331, 346]\n",
    "# 删除元素\n",
    "delete_elem_list = [23, 38, 47, 134, 161, 163, 196, 283, 345, 347, 350, 352]\n",
    "# 不管\n",
    "keep_going_list = [27, 162]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 313 删除的空值变量\n",
    "ops.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "for del_col in delete_elem_list:\n",
    "    mask=(ops[del_col]==0)\n",
    "    ops.iloc[mask, del_col]=np.nan\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 直接删除空值元素\n",
    "ops.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [],
   "source": [
    "for del_col in delete_col_list:\n",
    "    ops.pop(del_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "ops_ = SimpleImputer(strategy='mean').fit_transform(ops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [],
   "source": [
    "ops = pd.DataFrame(ops_, columns =ops.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [],
   "source": [
    "ops.columns = [f\"op{i}\" for i in ops.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对df列进行重命名并将ops整合进去"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_df.pop(\"time\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pre_df.iloc[:,:7].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.columns=[f\"raw{i}\" for i in range(7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pre_df.iloc[:,7:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.columns = [\"S\", \"RON\", \"RON_loss\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pre_df.iloc[:,10:14].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.columns = [f\"prop{i}\" for i in range(4)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ = pd.concat([df1, df2, df3, ops], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_.to_csv(\"processed.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分析取值范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "op_df = pd.read_excel(\"附件四：354个操作变量信息.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.20-0.37\n",
       "1            10-35\n",
       "2        2.35-2.70\n",
       "3         600-1000\n",
       "4          410-435\n",
       "          ...     \n",
       "349     60-5500000\n",
       "350     0-40000000\n",
       "351    -14000-3500\n",
       "352     0-40000000\n",
       "353       -125-0.5\n",
       "Name: 取值范围, Length: 354, dtype: object"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op_df[\"取值范围\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from parse import compile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = compile(\"{}-{}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "parsed=op_df[\"取值范围\"].apply(lambda x: pattern.parse(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "op_df[\"lower\"]=pd.to_numeric(parsed.apply(lambda x:eval(x[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "op_df[\"upper\"]=pd.to_numeric(parsed.apply(lambda x:eval(x[1].replace(\"（\",\"(\").replace(\"）\",\")\"))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(354):\n",
    "    s = df.iloc[:, 15 + i]\n",
    "    min_ = s.min()\n",
    "    max_ = s.max()\n",
    "    op_df.loc[i, \"lower\"] = min(op_df.loc[i, \"lower\"], min_)\n",
    "    op_df.loc[i, \"upper\"] = max(op_df.loc[i, \"upper\"], max_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对285样本和313样本的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "d285 = pd.read_csv(\"285.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "d313 = pd.read_csv(\"313.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48 40\n",
      "83 40\n",
      "110 40\n"
     ]
    }
   ],
   "source": [
    "for i in range(354):\n",
    "    logic = ~np.logical_and(d285.iloc[:,i+1]>=op_df.loc[i,\"lower\"] , d285.iloc[:,i+1]<=op_df.loc[i,\"upper\"])\n",
    "    n_invalids = (logic.sum())\n",
    "    if n_invalids>0:\n",
    "        print(i, n_invalids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(d285.var()==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 355)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d285.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(354,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(d285.var()==0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 11)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d285[d285.columns[np.all(d285==0, axis=0)]].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "d285_ = d285.drop(\"time\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "d313_ = d313.drop(\"time\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 24,  45,  73,  95, 109, 150, 204, 207, 215, 298, 331])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(354)[np.all(d285_==0, axis=0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['新氢进装置流量', '1#催化汽油进装置流量', 'D121顶去放火炬流量', '新氢进装置流量.1', 'Unnamed: 110',\n",
       "       'D-109松动风流量', '进料调节阀旁路流量', '紧急氢气去D-102流量', '补充氢压缩机出口返回管流量',\n",
       "       'EH-102加热元件/A束温度', 'D-201含硫污水排量'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d285_.columns[(d285_==0).sum()>10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "新氢进装置流量            40\n",
       "1#催化汽油进装置流量        40\n",
       "D121顶去放火炬流量        40\n",
       "新氢进装置流量.1          40\n",
       "Unnamed: 110       40\n",
       "D-109松动风流量         40\n",
       "进料调节阀旁路流量          40\n",
       "紧急氢气去D-102流量       40\n",
       "补充氢压缩机出口返回管流量      40\n",
       "EH-102加热元件/A束温度    40\n",
       "D-201含硫污水排量        40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(d285_[d285_.columns[(d285_==0).sum()>=1]]==0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "新氢进装置流量            40\n",
       "1#催化汽油进装置流量        40\n",
       "新氢进装置流量.1          40\n",
       "Unnamed: 109        2\n",
       "D-109松动风流量         40\n",
       "D-106热氮气流量         16\n",
       "Unnamed: 175        6\n",
       "进料调节阀旁路流量          40\n",
       "紧急氢气去D-102流量       40\n",
       "补充氢压缩机出口返回管流量      40\n",
       "3.0步骤FIC2432.SP    16\n",
       "EH-102加热元件/A束温度    40\n",
       "D-121含硫污水排量         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(d313_[d313_.columns[(d313_==0).sum()>=1]]==0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 313 部分空值填充均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "column = \"Unnamed: 109\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f7e9bf3088>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=d313_[\"Unnamed: 109\"]; plt.plot(s.index, s.tolist()) # 2 zeros [24, 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (d313_[column]==0)\n",
    "d313_.loc[mask, column] = d313_.loc[~mask, column].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f7e9c661c8>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=d313_[column]; plt.plot(s.index, s.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "column = \"D-121含硫污水排量\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f7e9d491c8>]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=d313_[column]; plt.plot(s.index, s.tolist()) # 2 zeros [24, 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (d313_[column]==0)\n",
    "d313_.loc[mask, column] = d313_.loc[~mask, column].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f7e9dbb248>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=d313_[column]; plt.plot(s.index, s.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 313 部分空值变量应该删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f7e979c4c8>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=d313_[\"D-106热氮气流量\"]; plt.plot(s.index, s.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f7e9811688>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=d313_[\"Unnamed: 175\"]; plt.plot(s.index, s.tolist()) # 6 zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f7e987bb48>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=d313_[\"3.0步骤FIC2432.SP\"]; plt.plot(s.index, s.tolist()) # 16 zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([161, 174, 283])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(354)[d313_.columns.isin([\"D-106热氮气流量\", \"Unnamed: 175\", \"3.0步骤FIC2432.SP\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "ops.iloc[312,[161, 174, 283]] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "D-106热氮气流量          36.018813\n",
       "Unnamed: 175       162.596557\n",
       "3.0步骤FIC2432.SP     37.024794\n",
       "dtype: float64"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d313_m[[161, 174, 283]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time                   2017/5/15 8:00:00\n",
       "硫含量,μg/g                             392\n",
       "辛烷值RON                              90.3\n",
       "饱和烃,v%（烷烃+环烷烃）                     55.05\n",
       "烯烃,v%                              20.89\n",
       "                             ...        \n",
       "8.0MPa氢气至循环氢压缩机入口                1943.69\n",
       "8.0MPa氢气至循环氢压缩机入口.1          2.14949e+06\n",
       "8.0MPa氢气至反吹氢压缩机出口                2501.85\n",
       "8.0MPa氢气至反吹氢压缩机出口.1          2.15416e+06\n",
       "D101原料缓冲罐压力                     -113.376\n",
       "Name: 313, Length: 369, dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[313,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "氢油比                       0.261936\n",
       "反应过滤器压差                    17.1835\n",
       "还原器压力                      2.41706\n",
       "还原器流化氢气流量                  850.301\n",
       "反应器上部温度                    424.969\n",
       "                          ...     \n",
       "8.0MPa氢气至循环氢压缩机入口          1943.69\n",
       "8.0MPa氢气至循环氢压缩机入口.1    2.14949e+06\n",
       "8.0MPa氢气至反吹氢压缩机出口          2501.85\n",
       "8.0MPa氢气至反吹氢压缩机出口.1    2.15416e+06\n",
       "D101原料缓冲罐压力               -113.376\n",
       "Name: 313, Length: 354, dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[312,15:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86 14755121.25 14795000.0\n",
      "93 18346556.0 18300000.0\n",
      "98 21155841.0 21200000.0\n",
      "99 10469520.0 10500000.0\n",
      "103 48172662.0 48200000.0\n",
      "104 23661272.0 23700000.0\n",
      "108 47.222061350000004 49.707433\n",
      "329 47.222061350000004 49.707433\n",
      "347 87939278.25 87900000.0\n"
     ]
    }
   ],
   "source": [
    "not_eq = []\n",
    "for i,(a, b) in enumerate(zip(df.iloc[312,15:], d313_.mean())):\n",
    "    if not f\"{a:.04}\"==f\"{b:.04}\":\n",
    "        print(i, a, b)\n",
    "        not_eq.append(i)\n",
    "#     print(f\"{a:.04}\\t{b:.04}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 3\n",
      "38 1\n",
      "42 20\n",
      "43 4\n",
      "58 2\n",
      "62 5\n",
      "85 2\n",
      "132 1\n",
      "155 8\n",
      "156 10\n",
      "161 6\n",
      "174 8\n",
      "209 3\n",
      "212 3\n",
      "216 14\n",
      "218 4\n",
      "220 3\n",
      "240 3\n",
      "241 4\n",
      "242 7\n",
      "243 6\n",
      "247 2\n",
      "291 9\n",
      "316 7\n",
      "322 1\n"
     ]
    }
   ],
   "source": [
    "cur_df = d313_\n",
    "invalids = []\n",
    "all_invalids=[]\n",
    "for i in range(354):\n",
    "    logic = ~np.logical_and(cur_df.iloc[:,i]>=op_df.loc[i,\"lower\"] , cur_df.iloc[:,i]<=op_df.loc[i,\"upper\"])\n",
    "    n_invalids = (logic.sum())\n",
    "    if n_invalids>0:\n",
    "        print(i, n_invalids)\n",
    "        all_invalids.extend(np.arange(40)[logic])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除313中的超出范围的异常样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_df = d313_\n",
    "invalids = []\n",
    "invalid_degrees = np.zeros([40])\n",
    "for i in range(354):\n",
    "    logic = ~np.logical_and(cur_df.iloc[:,i]>=op_df.loc[i,\"lower\"] , cur_df.iloc[:,i]<=op_df.loc[i,\"upper\"])\n",
    "    n_invalids = (logic.sum())\n",
    "    if n_invalids>0:\n",
    "        for j in range(40):\n",
    "            if cur_df.iloc[j,i] < op_df.loc[i,\"lower\"]:\n",
    "                exceed = op_df.loc[i,\"lower\"] - cur_df.iloc[j,i]\n",
    "            elif cur_df.iloc[j,i] > op_df.loc[i,\"upper\"]:\n",
    "                exceed = cur_df.iloc[j,i] - op_df.loc[i,\"upper\"]\n",
    "            else:\n",
    "                exceed = 0\n",
    "            invalid_degrees[j] += exceed / (op_df.loc[i,\"upper\"] - op_df.loc[i,\"lower\"])\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.98628265e+00, 1.86821617e+00, 1.53131904e+00, 1.51467079e+00,\n",
       "       1.29830440e+00, 1.05739038e+00, 8.76192771e-01, 8.66199545e-01,\n",
       "       7.70276000e-01, 6.58064620e-01, 6.04762782e-01, 4.75432321e-01,\n",
       "       4.66998655e-01, 4.57913571e-01, 4.54182877e-01, 4.47111764e-01,\n",
       "       4.41699726e-01, 4.40394643e-01, 4.30023264e-01, 4.25066526e-01,\n",
       "       4.05357143e-01, 1.94649507e-01, 1.07119400e-01, 8.88135411e-02,\n",
       "       8.57846939e-02, 8.48089307e-02, 7.73301667e-02, 6.88826667e-02,\n",
       "       6.58551333e-02, 6.28662177e-02, 6.10434966e-02, 5.27568333e-02,\n",
       "       5.17716071e-02, 5.00271667e-02, 4.64777333e-02, 1.79371667e-02,\n",
       "       1.76900000e-02, 2.12000000e-03, 4.67111111e-04, 0.00000000e+00])"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-np.sort(-invalid_degrees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18, 27, 25, 10, 17, 16], dtype=int64)"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(-invalid_degrees).argsort()[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[27, 19, 37, 18, 25, 7, 8, 16, 26, 36]"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[item[0] for item in Counter(all_invalids).most_common()[:10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10, 16, 18, 25, 26, 27, 35, 36, 37}"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([item[0] for item in Counter(all_invalids).most_common()[:15]])  &   set((-invalid_degrees).argsort()[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "d313_m = d313_.iloc[np.setdiff1d(np.arange(40), [18, 27, 25, 10, 17, 16]), :].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用 pauta criterion 删除异常样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "cur_df = d285_\n",
    "\n",
    "mean = cur_df.mean()\n",
    "std = cur_df.std()\n",
    "cnt = 0\n",
    "for i, sample in cur_df.iterrows():\n",
    "    if np.any(np.abs(sample - mean) > std * 3):\n",
    "        print(i)\n",
    "        cnt += 1\n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "3\n",
      "4\n",
      "5\n",
      "7\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "15\n",
      "17\n",
      "20\n",
      "21\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "31\n",
      "32\n",
      "34\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "27\n"
     ]
    }
   ],
   "source": [
    "cur_df = d313_\n",
    "\n",
    "mean = cur_df.mean()\n",
    "std = cur_df.std()\n",
    "cnt = 0\n",
    "invalids = []\n",
    "for i, sample in cur_df.iterrows():\n",
    "    if np.any(np.abs(sample - mean) > std * 3):\n",
    "        print(i)\n",
    "        invalids.append(i)\n",
    "        cnt += 1\n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "d313_m = d313_.iloc[np.setdiff1d(np.arange(40), np.union1d([18, 27, 25, 10, 17, 16], invalids)), :].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  6,  8, 14, 19, 22, 29, 30, 33, 35])"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.setdiff1d(np.arange(40), np.union1d([18, 27, 25, 10, 17, 16], invalids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.union1d([18, 27, 25, 10, 17, 16], invalids).size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将285与313的数据赋值到df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(d313_m).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "氢油比                    2.621217e-01\n",
       "反应过滤器压差                1.722987e+01\n",
       "还原器压力                  2.416984e+00\n",
       "还原器流化氢气流量              8.505484e+02\n",
       "反应器上部温度                4.249020e+02\n",
       "                           ...     \n",
       "8.0MPa氢气至循环氢压缩机入口      2.023978e+03\n",
       "8.0MPa氢气至循环氢压缩机入口.1    2.149370e+06\n",
       "8.0MPa氢气至反吹氢压缩机出口      2.494629e+03\n",
       "8.0MPa氢气至反吹氢压缩机出口.1    2.153988e+06\n",
       "D101原料缓冲罐压力           -1.133767e+02\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d313_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "d285_m = d285_.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "氢油比                    2.733991e-01\n",
       "反应过滤器压差                2.420824e+01\n",
       "还原器压力                  2.528870e+00\n",
       "还原器流化氢气流量              8.558825e+02\n",
       "反应器上部温度                4.215093e+02\n",
       "                           ...     \n",
       "8.0MPa氢气至循环氢压缩机入口      2.200789e+03\n",
       "8.0MPa氢气至循环氢压缩机入口.1    5.149259e+06\n",
       "8.0MPa氢气至反吹氢压缩机出口      2.846897e+03\n",
       "8.0MPa氢气至反吹氢压缩机出口.1    5.984749e+06\n",
       "D101原料缓冲罐压力           -9.721070e+01\n",
       "Length: 354, dtype: float64"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d285_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[312, 15:] = d313_m.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[284, 15:] = d285_m.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 探索性建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"raw_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"raw_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PowerTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import QuantileTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sample_index\n",
       "1      1.38\n",
       "2      1.18\n",
       "3      1.38\n",
       "4      1.38\n",
       "5      1.28\n",
       "       ... \n",
       "321    1.15\n",
       "322    1.25\n",
       "323    1.35\n",
       "324    1.28\n",
       "325    1.25\n",
       "Name: RON损失\\n（不是变量）, Length: 325, dtype: float64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df[\"RON损失\\n（不是变量）\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_2 = X_.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = X_2.pop(\"RON_loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA, FastICA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting lightgbm\n",
      "  Downloading lightgbm-3.0.0-py2.py3-none-win_amd64.whl (737 kB)\n",
      "Requirement already satisfied: numpy in c:\\software\\miniconda3\\lib\\site-packages (from lightgbm) (1.19.1)\n",
      "Requirement already satisfied: scipy in c:\\software\\miniconda3\\lib\\site-packages (from lightgbm) (1.5.2)\n",
      "Requirement already satisfied: scikit-learn!=0.22.0 in c:\\software\\miniconda3\\lib\\site-packages (from lightgbm) (0.23.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\software\\miniconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (2.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\software\\miniconda3\\lib\\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.16.0)\n",
      "Installing collected packages: lightgbm\n",
      "Successfully installed lightgbm-3.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.astype(float).to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "     X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet, BayesianRidge, Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2941971260876282"
      ]
     },
     "execution_count": 573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trans.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [],
   "source": [
    "selector = SelectFromModel(estimator=GradientBoostingRegressor(random_state=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (1000) is greater than the total number of samples (325). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n"
     ]
    }
   ],
   "source": [
    "X_ = QuantileTransformer(n_quantiles=1000).fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ = selector.fit_transform(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ = selector.fit_transform(X_, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(325, 26)"
      ]
     },
     "execution_count": 645,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "     X_, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "#     (\"scale\", StandardScaler()),\n",
    "#     (\"scale2\", RobustScaler()),\n",
    "    (\"transform\", QuantileTransformer(n_quantiles=1000)),\n",
    "#     (\"select\", SelectFromModel(estimator=GradientBoostingRegressor(random_state=0))),\n",
    "#     (\"pca\", PCA(n_components=30)),\n",
    "#     (\"ica\", FastICA(n_components=15)),\n",
    "     (\"regressor\", LGBMRegressor(random_state=0, n_estimators=100, learning_rate=0.1)),\n",
    "#     (\"regressor\", BayesianRidge(normalize=True)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (1000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (1000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (1000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (1000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (1000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.38709457699393846"
      ]
     },
     "execution_count": 649,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(pipeline, X_, y, cv=cv).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    (\"scale\", StandardScaler()),\n",
    "    (\"scale2\", RobustScaler()),\n",
    "    (\"transform\", QuantileTransformer(n_quantiles=2000)),\n",
    "#     (\"select\", SelectFromModel(estimator=GradientBoostingRegressor(random_state=0))),\n",
    "    (\"pca\", PCA(n_components=30)),\n",
    "    (\"ica\", FastICA(n_components=15)),\n",
    "#      (\"regressor\", LGBMRegressor(random_state=0, n_estimators=100, learning_rate=0.1)),\n",
    "    (\"regressor\", BayesianRidge(normalize=True)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.16148203548560197"
      ]
     },
     "execution_count": 625,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(pipeline, X_, y, cv=cv).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(325, 349)"
      ]
     },
     "execution_count": 623,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (1000) is greater than the total number of samples (217). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.11742715140447113"
      ]
     },
     "execution_count": 640,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train, y_train)\n",
    "pipeline.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (217). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.12825366796013127"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train, y_train)\n",
    "pipeline.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = KFold(n_splits=5, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n",
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (260). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.24387199, 0.08346677, 0.21650902, 0.13021928, 0.08092405])"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(pipeline, X, y, cv=cv).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15099822053057504"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\software\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:2370: UserWarning: n_quantiles (2000) is greater than the total number of samples (325). n_quantiles is set to n_samples.\n",
      "  % (self.n_quantiles, n_samples))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scale', StandardScaler()),\n",
       "                ('transform', QuantileTransformer(n_quantiles=2000)),\n",
       "                ('select', SelectFromModel(estimator=ExtraTreesRegressor())),\n",
       "                ('pca', PCA(n_components=30)),\n",
       "                ('ica', FastICA(n_components=15)),\n",
       "                ('regressor', BayesianRidge(normalize=True))])"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipeline.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f780e7d248>"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score, mean_squared_error, median_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2551673906107038"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f780e70e48>"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc-autonumbering": true,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
